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The analysis is performed taking as a reference the components that integrate the communication framework theory-the message, the form, the encoder, the context, the channel, and the decoder-which correspond to six elements in the context of data visualization: content, graphic representation, encoding setup, graphic design and approach, media, and user.

The unfolding of these dimensions is undertaken following a common pattern of six organizational layers of complexity-basic, extended, synthetic, dynamic, interactive, and integrative-according to the analytical criteria. Free full text. Front Res Metr Anal. Published online Apr PMID: Author information Article notes Copyright and License information Disclaimer. This article was submitted to Research Assessment, a section of the journal Frontiers in Research Metrics and Analytics.

Received Dec 18; Accepted Feb 9. The use, distribution or reproduction in other forums is permitted, provided the original author s and the copyright owner s are credited and that the original publication in this journal is cited, in accordance with accepted academic practice.

No use, distribution or reproduction is permitted which does not comply with these terms. Abstract This article consists of a conceptual analysis—from the perspective of communication sciences—of the relevant aspects that should be considered during operational steps in data visualization. Keywords: index terms: data visualization, dimensional taxonomy, communication process, communication theory, knowledge transfer, complexity.

Introduction Complexity as a Challenging Parameter to Integrate in Data Visualization Over the past decades, visualization and complexity have received extensive scientific attention, and there has been a huge increase in the number of publications dealing directly or indirectly with their relation. The Lack of an Integral Data Visualization Taxonomy to Tackle Complexity Data visualization and complexity as scientific topics are undergoing a period of consolidation with an increasing and overwhelming number of scientific publications and specialists working on these fields.

However, along with this positive impression, a more detailed overview suggests that linked problems remain unsolved: 1 From the object side, at any scientific discipline where the concept of complexity appears, it refers to objects constituted by interconnected layered networks; however, there is not a common proposal for a pattern of complexity in phenomena from both an organizational and analytical perspective.

Communication Components and Layers of Complexity in the Data Visualization Process Any scientific research inquiry follows three procedural stages when managing data: data formalization, data analysis, and data visualization, which, respectively, transform observations and measurements into data, data into information, and information into knowledge.

The first step to start a thorough review of these factors is to identify the following elements that participate in data visualization understood as a communication process: – the content , the data, and information to be communicated – the graphic representation of this content – the encoding of the information integrating data and graph specifications – the design adapted to the context , the audience, or the target – the media by which the visualization is published and disseminated – the user who receives the visualization The proposal of these elements is not arbitrary.

Furthermore, understanding the completion of these actions as a critical success factor, they must be undertaken considering their interconnection which plays a critical role and can be expressed by means of the following practical questions: 1 What content does the sender want to communicate and to what degree of abstraction?

Objectives and Method: Building a Taxonomy The above questions highlight six dimensions of the communication process that, conditioning the systematic procedure of data visualization, must be accurately studied: – the degrees of abstraction of the information – the functionalities of the tool for the graphical representation – the specifications for the setup of the visualization – the approach modes to the context by an appropriate graphic design – the levels of communication efficiency in the media – the requirements of the visualization perceived as values from the user experience side The definition of these dimensions leads to the equally important issue of internal order in which they must be unfolded.

From previous studies about data analytical procedure Cavaller, ; Cavaller, , it has been shown that, as a general rule, the construction of indicators applied to data analysis is correlated with the layers of organizational complexity that exist in any organized entity or phenomenon: 1 Basic layer: basic interactions 2 Extended layer: multivariate relationships 3 Dynamic layer: distributions or multi-relational dynamic 4 Synthetic layer: internal logics or processes 5 Interactive layer: system as architecture of hyper-processes 6 Integrative layer: organization as ecosystem Given that the layers of complexity of any object or phenomenon condition the structure of the analytical procedure, data analysis imposes a scale approach on data visualization in an object-centered way.

Taking this conception as a starting point of the review and the analysis, the goal of this article was to design an object-centered data visualization model , organized in two axes: – as a set of gradual approaches to the complexity of the dimension that is managed – by means of the progressive completion of the corresponding communication component As a result, a dimensional taxonomy of data visualization based on a matrix structure—where the elements that participate in data visualization act as factors of completeness, and their development in layered dimensions act as factors of complexity—is proposed see Table 1.

TABLE 1 Matrix architecture of factors of completeness and complexity for the design of the dimensional taxonomy of data visualization according to the components of the communication framework theory. In which form? Which functionalities from which tools are appropriate for the graphical representation that is pursued? What specifications must be applied to the setup of both data and graphical representation in order to adapt each other?

What are the approach modes and the graphical design suitable to the context, target or audience? What characteristics must be contemplated to achieve the levels of communication efficiency according to the media? What requirements must be observed from the user’s experience in order to improve understanding of the topic? Open in a separate window. Content and Degrees of Abstraction of Information The first node of the communication framework is the message or the content of the communication.

Parameters, Sample, and Descriptive Statistics In practical terms, data visualization can be faced with three potential initial scenarios: a requirement of data visualization without previous data formalization, without previous data analysis, or, in the best case, with both data formalization and analysis previously performed.

Clustering of Parameters: The Construction of Indicators as Evidenced Relations A second degree of abstraction of information is reached when the requirement for data visualization needs adding and accumulating new observed properties about the subject to the focus. Conceptual Synthesis and Symbolic Abstraction of a Process In case of wanting to visualize a complex phenomenon, usually associated to a process, the definition of the parameters, the construction of indicators, or the detection of interconnected factors or patterns is not enough because the abstraction required is an, more than probable, explanation.

Layered Processes, Hyper-Processes, and Systems: Experimentation and Testing Hypothesis When considering systems where hyper-processes—resulting from the coexistence of interconnected processes—are involved, a higher degree of abstraction in information must be achieved. Abstraction in Scientific Modeling as a Reconstruction of an Organization The degree of abstraction of the information is correlated with the complexity of the entity from which data have been obtained and data visualization has to show.

Basic Functionalities for a Descriptive Graphical Representation The basic functionalities in the graphical representation of data are associated with a descriptive visualization of the parameters that depict a phenomenon. Advanced Functionalities for a Relational Graphical Representation Multivariate or relational visualization involves the observation of multiple measurements and their relationship.

Functionalities for a Graphical Representation in a Dynamic Visualization Dynamic or multi-relational visualization represents a reality where all the factors—defined as set of related parameters—are interconnected, and therefore there is interdependence between them, and consequently, their network position changes according to a spatial or temporal joint distribution.

Process Graph, Info Graphics, and Motion Graphics: Representing Processes Process visualization must describe the internal logics that lie behind phenomena. Convergence of the Symbolic and Analytical Path: Integration of Scientific Data Visualization Multidimensional phenomena structured in different layers of processes, where different organizational systems are involved, make their graphic representation an extremely complex matter. Encoding Specifications and Configuration Settings The third node of the communication framework is the encoder, and its action is the encoding or the communication configuration.

Data Formalization Adhoc. Setting up Data and Plotting Elements for Descriptive Visualization The first step in the basic configuration of data visualization is to specify and verify basic elements selected such as parameters, constants and variables, scale, data range, sample, legend, and labels, and to check them in preliminary views in order to manipulate and ensure the accuracy of the representation.

Configuration of Data and Representation of Dynamic Multidimensional Distribution in Data Visualization: Integration of Applications The next step in the process of configuring data visualization focuses on the dynamic relationships and distributions between groups of variables, combining and communicating different visualization techniques and methodologies, which generates a fundamental requirement for dynamic, compatible, and interconnected tools for visual encoding.

Configuring Data Process Visualization Following consecutive levels of complexity, the configuration is directed to the design and programming of algorithms that simulate the operation of the logical structure of the process that underlies the phenomenon to be represented graphically. Specifications for the Configuration of the Data and Its Graphical Representation in an Interactive Visualization Getting into the internal complexity of phenomena involves defining the different layers of sub- or super-processes that participate or overlap in strata, which in turn requires developing and mastering complex visualization tools.

Display Settings for Visual Reconstruction In the comprehensive visual reconstruction of an organization, the convergence of data visualization and data analysis has become indispensable. Graphic Design and Context: Modal Approaches and Properties of Good Data Visualization The fourth node of the communication framework is the context, which in data visualization is developed by graphic design.

Subjective Approach Visualization must be meaningful. Objective Approach Data visualization plays a critical role in multiple professional and academic fields, which means that it needs to adapt to particular specifications. Commercial Approach and Persuasive Communication In the commercial approach, the graphic designer does not only try to capture the attention and interest of the user but also tries to convince him of the benefits of a product and a service.

Educational-Investigative Approach In contexts where learning or research processes take place, the design of data visualization is a factor of great importance. Scientific Approach The graphic design of data visualization in a scientific approach is a challenge that can be explained by different perspectives. Media and Levels of Communication Efficiency The fifth function of data visualization is to communicate relevant and objective information—understood as knowledge—in the most efficient way through the appropriate media.

Content Editing: Correctness, Completeness, Timeliness, Accuracy, Review, and Control The communication efficiency in editing the content of data visualization is measured in relation to its correctness, completeness, timeliness, accuracy, form, purpose, proof, and control. User and Usability Requirements In the visualization process and as a culmination of it, the requirements arising from the interaction and user experience must be considered, which are defined as components of usability.

Feedback, Interaction, and Error Prevention: Supportiveness and Robustness The evaluation of the usability of data visualization tools can be carried out by studying the errors made by the user with the objective of introducing improvements for future prevention and for enhancing their robustness. Results The results of the study conducted in this article can be classified into two groups: theoretical— which include a dimensional factors, b characterization of achievements—and practical , which include c types of data visualization, d functions, e principles of assessment, and f professional competences of data visualization.

TABLE 2 Dimensional taxonomy of data visualization: factors of completeness and factors of complexity. TABLE 4 Variables, types of visualization, and graphical representation by goals from the perspective of an object-centered data visualization model Cavaller et al. Content-variable Types of data visualization and graphical representation Object-goal 1 Measurements Descriptive Parameters and basic relationships 2 Indicators Relational Multivariate relationships 3 Distributions Multi-relational dynamics Factors or multi-relationships 4 Flow: vector Process Internal logics 5 Network: connector Hyper-process: system Architecture 6 Program: code Ecosystem Organization.

TABLE 5 Taxonomy of data visualization: functions, principles, and competences in data visualization. Findings and discussion The fundamental conceptual findings of the study include the following: 1 The process of data visualization can be viewed from the perspective of communication sciences, which includes six major components: message, form, encoder, context, channel, and decoder.

Representation of the sequentiality of data processing and innovation hyper-cycle. Author Contribution The author confirms being the sole contributor of this work and has approved it for publication. Conflict of Interest The author declares that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.

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Editors Howes D. Usabilidad web. This raises issues of data integrity, that is, the provenance information may be lost when copying just the test results data.

We begin with an overview of our approach, then present three of its distinctive features. While Python is portable, its performance is similar to that of other byte-code compiled languages i.

The GMark-RI Workload Modeler is an extension of the analysis tools that we have developed for the analysis of four grid traces [Ios06a]. The main additions are the automation of the analysis and modeling process, and a mechanism for representative area selection, described in Section 5. The GMark-RI can replay traces from various production environments. However, since a real trace contains tens of thousands to millions of jobs and span periods of months to years, a truncation process that selects a part of the input trace a trace area is required.

We have implemented two alternative mechanisms for job selection. First, we have developed a mechanism for representative area selection see Section 5. Both these databases are maintained by and considered representative for their respective communities. The unit generators produce detailed submission information for each application in the workload.

The appropriate unit generator is selected and instantiated by the Work- load Generator from the Applications Database. New generators can be added in the Applications Database. New printers can be added though a plug-in system. We also support other syn- thetic and real applications; we have performed tests with over 40 real C, Java, and MPI applica- tions [Moh05b; Ios06b; Son06].

During the submission of the jobs, it reports all job submission commands, the turnaround time of each job, including the grid overhead, the total turnaround time of the workload, and various statistical information.

The GMark-RI Data Manager archives and analyzes the test results, and produces an SQL re- lational database stores: project, sub-project, and test description and summaries, process informa- tion e. The analyzer processes the database and produces detailed reports, some of which we present in Section 5. To ease this task, we have designed an extensible workload description language. Lines are used to generate sequential jobs of types sser and sserio, with default parameters.

Internally, the sser and the sserio unit generators generate the individual jobs data. Based on this information, the printer for the tested system, e. All four job types follow a Poisson arrival process, with an average arrival rate of 1 job every seconds. The framework design can be easily extended, for instance by adding various workload generation no- tions e. The plug-in developer can decide to use these library functions, or to parse the extension language statements independently.

A default weight of 1. We call the representative area from a trace T , with length L and with target characteristics C, the subset of length L from trace T whose characteristics are the closest to C from all possible subsets of length L from trace T. We have developed a window-based mechanism that selects from an input trace and a set of characteristics the representative area of length equal to the window size W , and which starts only at discrete multiples of the window size.

Consider that a user needs to perform a test that requires four hours of realistic workload input; the user has a one-year trace that is representative for the test. We show in this section that GMark-RI operates with low overhead and submission delays, ensuring that the tests are are re-performed in very similar experimental conditions design goal 2a.

The jobs in one run are submitted with a 1-second inter-batch delay WL submission component in Figure 5. The test continues after the last submission for one hour WL execution component in Figure 5. After one hour, all jobs still running or waiting in the system queue are stopped, and the results of the test are processed post-processing component in Figure 5. The individual averages are equally spread around the centerline. We conclude that the submission process is controlled [She80], ensuring that the same test can be re-performed in practically identical experimental conditions.

Workload Generator B. We test the performance of the GMark-RI Workload Generator for workload sizes of up to , jobs on the experimental platform described in Table 5. We generate only all-zeroes input data. We let the submitter complete the submission of the workload, then estimate the average submission delay the accuracy. Using 5 submission threads the default GMark-RI setting , the submission delay remains on average below 2 ms.

The 1st and the 3rd quartiles of the submission delay remain below 4 ms, which is well below the target of 0. There is no correlation between the submission delay and the workload size. Given that the measurement precision required for grid computing systems is 1 second, we conclude that the job submission delay of the GMark-RI workload submitter is negligible.

With only threads, the workload submitter cannot cope with the high arrival rate. Adding more than 75 submission threads decreases the accuracy of the submitter. A setup with submission threads 3- 19 per processor is optimal for our experimental platform. Table 5. Scenario Metrics Graphs Ref. Grid performance and reliability evaluation 1. Single- and mixed-application workloads a.

Bursty arrivals U U over time [Ios06b; Moh05b] 3. Grid settings comparison a. Single- vs. Unitary vs. Application- and Service-level performance a. Grid functionality testing 1. Ability to execute diverse workloads a. Single-application workloads FR [Ios06b; Moh05b] b. Mixed-applications workloads FR [Ios06b; Moh05b] c.

Reliability under stress-load FR [Ios06b] 2. Stability, quality control, other functionality over time a. Periodic functionality testing FR [Ios06b] C.

Peer-to-Peer functionality and reliability evaluation 1. Functionality testing a. Protocol correctness CI,Rec Rec vs. CI [Roo06] 2. Reliability testing a.

DTA over time [Roo06] b. DTA [Roo06] , test jobs in the last 18 months, in over 25 fully-automated testing scenarios. We summarize 13 of these scenarios in this section; 7 in Section 5. We do not report here results of testing peer-to-peer systems; for more details we refer the reader to [Roo06]. Unless otherwise noted, each of the experiments in this section demonstrates that GMark-RI meets the design goals 1, 2a, 3a, and 4b.

We demonstrate meeting the design goals 2b and 3b in Sections 5. With the whole Section 5. The processors used in these experiments are managed with Condor. System Performance The experiments in this section prove the ability of GMark-RI to manage test results design goal 3b , both by aggregating results for the overall set of tests Figure 5. Note that GMark-RI is used here to compute both job-level and operational-level metrics. The user obtains a high rate of goodput even in a production environment: over 0.

Condor is fair with respect to resource consumption, and the throughput and goodput rates are halved after the user exceeds his quota at the beginning of 31 Aug It has been argued that to succeed in the industry, grids have to be predictable [Ken02], or depend- able [Fos98], or both [Ken03]; the ability of GMark-RI to assess performance metrics over time is critical in assessing the dependability and the predictability of the system.

This shows that some jobs get better treatment in Condor than others. Overall, there is a trend for jobs arriving later to wait more than the jobs arriving earlier. There appears to be no correlation between the average wait time and the run index, i. Run vs. Wait Time Wait Time [s] Wait Time [s] 0 0 0 0 Run Time [s] Run Time [s] Test jobs Test jobs cluster Figure 5.

Larger circles represent clusters with more jobs. Only clusters of at least jobs are depicted. System Fairness We investigate the slowdown fairness with which Condor dispatches jobs, where the slowdown of a job is expressed as the ratio between its run time and its response time wait time plus run time.

Ideally, the slowdown is identical for small and long jobs. Similarly, Figure 5. Furthermore, the variation of wait time does not change with the variation of run time.

Thus, the Condor job dispatching is overall not slowdown fair. Ideally, Condor will assign an equal number of jobs per managed machine. Pareto chart of the distribution of jobs per host. The real and the ideal job distributions have similar CDFs. It is therefore better to send jobs in small batches. Application- and Service-level Performance Figure 5.

The tested system achieves an overall application-level throughput of over 15 million states explored per second, and an overall service-level throughput of over 0. The decrease becomes predictable after 6 hours.

This allows a service provider to reliably guarantee service levels, even with the Condor fair use policy in place. These experiments demonstrate the ability of GMark-RI to compute application- and service-level metrics, that is, metrics that depend on results reported by the user application. This is done through the Data Manager plug-in system. Two types of test jobs are considered during the experiments: jobs that explore a normal-sized space normal instances , and jobs that explore a large-sized space large instances.

The normal jobs have a much better service-level goodput for dimen- sions up to 7. For dimensions of 8 through 12, both the normal and the large jobs behave similarly. For dimensions of 13 and higher, the large jobs become the best choice. These experiments demonstrate the ability of GMark-RI to compute service-level metrics that depend on the job parameters.

Unless otherwise stated, we use the success rate of the jobs as the performance metric for the following experiments. Another goal for these experiments is demonstrating that GrenchMark and GMark-RI can be used in a variety of testing scenarios. For all the following tests we have used the DAS system3 as an experimental environment. Grid jobs enter the Koala queue, and are redirected to the local cluster queues. Additionally, jobs may be sent directly to the local cluster queues, by-passing Koala.

Workload Submit rate scaling No. Workload No. We used both modeled and real trace-based workloads as test workloads. Each test is stopped 20 minutes after the submission of the last job. Thus, some jobs did not run, as not enough free resources were available during the time between their submission and the end of the workload run. First, we consider the case where a testing environment would be placed under much more strain.

The resource management middleware of the clusters in our testing environment has been replaced, due to the problems when faced with an increasing number of job submissions. Such a change could have been prevented if the question What if the current users would submit 10 times more jobs in the same amount of time?

We take a thoroughly studied trace [Li05] of the DAS system and re-run it in the new environment. The trace was recorded when the previous resource manager was still in place, and contains over , jobs run throughout We also scale the jobs submission times to make them 10, 25, 50, and times smaller than the original submission times. We conclude that the new resource management system including Koala can handle without a decrease of reliability an up to 10 times increase in the job arrival rate, for the user base characterized by the input traces.

Second, we ask the question: If the users of another environment could submit their workload to our testing environment, what would be the success rate of the jobs submitted by these combined communities?

This situation occurs when from two existing production environments one environment is put temporarily or permanently out of production, and only one environment remains to run the submissions of jobs from both user communities.

We selected just the jobs with runtimes below seconds. We conclude that the DAS including Koala can handle the proposed combinations of communities, with a reasonable success rate for submitted jobs. Third, we consider the question What if the system is suddenly subjected to a large number of submitted jobs bursts?.

We restrict our tests to one of the DAS clusters fs4. Comparing grid settings This section presents two situations in which GrenchMark can be used for comparing grid settings.

We consider two important features of grids: co-allocation i. We further call single-site jobs the jobs that are not co-allocated, and unitary jobs the jobs that have no dependencies, and of which no other job depends. Executing single-site jobs should be simpler than ex- ecuting co-allocated jobs.

We use three workloads of jobs each, one with single-site jobs, one with the same jobs co-allocated at various sites, and one with larger jobs co-allocated at various sites.

This is due to the atomic reservation problem of co-allocation: when the resources cannot be reserved, local users can acquire resources selected for co-allocation just before they are claimed by the co-allocating scheduler. We consider a comparison between the success rate of unitary and of composite applications in a grid environment without reservation capabilities, and with or without fault tolerance. We call sub- jobs the jobs that are part of a composite job; we consider that all sub-jobs of a composite job are submitted at the same time.

We consider only composite applications for which the sub-jobs are unitary applications. Since Koala does not support the execution of composite jobs, we have built a simple execution tool, which executes jobs that can run as soon as they become available.

With fault tolerance, for a system with a high success rate for jobs, e. In addition, GrenchMark has automatically provided answers for the most common test questions. First, GMark-RI has allowed performing a large variety of test scenarios that can be broadly grouped into three areas: grids, large-scale heterogeneous computing, and peer-to-peer systems.

For peer-to-peer systems see Table 5. Second, GMark-RI has greatly reduced the time needed to perform the tests. The selection of the appropriate test scenario depends very much on the human test designed, and may take anywhere from a few hours to a few days, regardless of the testing tool. However, creating the testing scenario after making the selection only takes a few minutes.

Similarly, changing the parameters of the scenario is also a matter of minutes. This greatly improves the currently prevailing practice in the LSDCS community of writing a new testing tool for every new occasion.

Enabling Testing Capabilities on Large-Scale Experimental Platforms Large-scale ex- perimental computer science platforms have been and are currently being built with the purpose of assessing the characteristics of computer systems in realistic conditions. Several such platforms already exist, e. While the middleware pre-installed on these testbeds includes deployment and coordination tools e. Thus, GrenchMark can provide for these testbeds support for real-life testing.

However, two research questions need to be further inves- tigated: a How to provide testing as a reliable service to the user in an unreliable, large-scale, and distributed system? Towards this end, we plan to cover two orthogonal issues: creating a grid workload model, and creating an LSDCS performance database. The main feature of GrenchMark, the ability to deal with grid workload traces, has been hampered until recently by the lack of traces.

Indeed, many organizations view this data as revenue-generating in the industry , or critical for obtaining grants academia , and are reluctant to make the data public. Jul Design Goals see Section 5. The second direction of work is the creation of an on-line, freely-accessible LSDCS performance database. First and foremost, various statistical techniques could be applied to the data within to gain insights into the causes of poor performance.

In particular, this approach would enable thorough work on performance models for grids and other LSDCSs. Compared to previous grid testing tools, GrenchMark focuses more on the testing process, with additional types of workload data sources, richer workload generation, and more detailed results analysis. These approaches have a limited applicability in grids, due to the heterogeneity and the dynamic state of grids.

Grid performance evaluation Few performance evaluation tools have been proposed in the context of grids [Rai06; Hoa07a]. Thus, relatively little focus has been given to realistic workload generation, and to detailed results analysis. The DiPerF [Rai06] is a distributed performance evaluation system that can be used to study the performance properties of Grid and Web services.

Grid functionality testing Several testing suites are available for functionality testing, based on the submission of single jobs or of simple workloads to the tested grid [Pav06; Chu04; Sma04; De 07; Ric04]. Internet testing The research road for testing the Internet and the World Wide Web has started with simple observations and characterization from a single point in the network, and is still evolving towards completely decentralized testing using realistic workloads.

We survey here only three recent advances. For more of the same, we refer the reader to [And02]. The National Internet Measurement Infrastructure NIMI project [Pax98] deals with problems related to scale: decentralized control of measurements, authentication and security, and deployment.

Peer-to-peeer testing The performance of peer-to-peer systems is mostly evaluated through analy- sis and simulation. Various P2P systems have been evaluated on custom environments built on top of Grid [Bus05; Nus07].

Acknowledgements We would like to thank the Politehnica University of Bucharest team in particular to Dr. Nicolae Tapus, to Corina Stratan, and to Mugurel Andreica for their help with extending the GrenchMark framework towards distributed testing and towards testing complete grid middleware stacks.

We are also grateful to the DAS-2 team in particular to K. Verstoep and P. Anita and to the Condor UWisc team in particular to Dr. Livny who kindly provided access to their computational environments for our tests. Understanding the causes and the extent of these problems is an impor- tant research topic. To address this problem we have presented in this chapter the GrenchMark framework for testing large-scale distributed computing systems.

The framework focuses on realistic and repeatable testing, and on enabling test results comparison across systems. Then, we have used GMark-RI in 25 fully-automated testing scenarios in the areas of performance evaluation, reliability testing, and functionality testing. Notably, we have performed hundreds of tests comprising hundreds of thousands of test jobs in large Condor-, Globus-, and BitTorrent-based environments; the results presented in this chapter have been obtained in real production systems.

In this process, simulation is critical to assess large numbers of experimental settings in a reasonable amount of time, and to design and evaluate the systems of the future. To address these issues, in this chapter we answer an important research question: How to build an adequate simulation framework for grids? In particular, many books [Fuj99; Law00; Ban01] and survey articles [Fis95; Per06] attest to the progress made in parallel and distributed discrete event simulation.

In Section 6. Then, we describe the resulting design and the main features of the DGSim framework in Section 6. Finally, in Section 6. The model may also include varying resource availability.

With grid systems being highly dynamic in the number and the size of their resources on both short and long term see also Section 6. Experiment Support for GRMs Perhaps the most limiting factor in the adoption of a simu- lation framework is the degree of automation.

The simulation frameworks should be able to run in an unreliable environment, i. In particular, the most common grid resource management metrics should be automatically extracted from the simulation results.

At the same time, given the high percentage of single processor jobs present in grid workloads [Ios06a], there is a need for a simulator to deal with workloads of tens of thousands to even millions of jobs.

The individual simulation time is not among these metrics, as it depends heavily on what is actually simulated, e. The Investigator formulates the problem and is interested in the results. The Experiment Designer is responsible for designing and performing a set of experiments that give answers to the problems raised by the Investigator.

The QA Team Member is responsible for validating the experiments, e. Figure 6. The main components of DGSim are: 1. After generating the environment and the workload, the Experiment Manager calls the Simulation component. The Environment and Workload Generation component automates the experimental setup. Similarly, the Workload Generation ensures that realistic workloads are generated or simply extracted from existing grid workload traces.

This component can also automatically extract parameters for a given model from an input grid workload trace. The Simulation component executes large numbers of individual simulations grouped by scenario and run. The computing power may be provided by one machine e. The Simulation component also collects all the results and stores them into the Simulation Results Database for future analysis.

The stored data are the simulation results and provenance data simulator parameters, version, and events. The Data Warehousing component is responsible for organizing data, and for data analysis. Data are indexed by experiment, by scenario, and by run. This component analyzes for all simulations the time series of job arrivals, starts, and completions, and produces statistical results for the common performance metrics, e. It also analyzes the messages exchanged by the simulated entities.

Additional analyzers may be used to process the data. Our model extends previous grid simulation approaches see Section 6. In previous work, all the jobs in a queue are considered as the input set of the scheduler; our job scheduling model also allows the selection of only some of the queued jobs, through a selection policy.

In current grid simulators, the resource management architecture is either purely centralized or purely decentralized. In comparison, the inter-operation model of DGSim also considers the hierarchical and the more realistic hybrid architectures. Finally, existing simulators have used an information model in which only one of the job runtime, the cluster dynamic status, and the resource speed is not accurately known.

In contrast, the information model of DGSim allows all the pieces of information used in the scheduling process to be inaccurate or even missing. The resource model already introduced in Chapter 2 assumes that grids are groups of clusters of homogeneous resources, e.

There is no logical connection between the use of various resource types hard-coded in the simulator. The clusters may have logical links with other clusters e. The workload model also introduced in Chapter 2 takes into account individual jobs, job grouping e. We assume the execution time of a job to be proportional to the speed of the processor it runs on. We therefore integrate in our simulation framework the concept of grid inter-operation.

For this purpose, we have implemented six architectural alternatives, of which four are depicted in Figure 6. The independent or isolated clusters architecture assumes there is no load exchange between the clusters. The centralized meta-scheduler assumes the load exchange is coordinated by a central node. With hierarchical meta- scheduler, the load exchange is coordinated by a hierarchy of schedulers.

The distributed meta- scheduler assumes there is no central control for load sharing and each cluster can exchange load with any other cluster.

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The principle of interest for efficiency states that data visualization is right in so far as it achieves the communication goals by the optimal means of communication with maximum benefits and minimal use of resources. The principle of appraisal interest states that data visualization is right in so far as it receives a positive assessment from the user in terms of usability and of other factors related to H—M interaction.

According to the functions and principles mentioned above, data visualization can be defined as a multidisciplinary field where professionals need a wide range of knowledge specializations and professional competences such as data analysis, data graphic representation, programming, graphic design, media publishing, and human—machine interaction.

The fundamental conceptual findings of the study include the following:. These layers, obtained by analytical criteria, indicate the degree of the internal complexity of the organized entity or a phenomenon that is represented, and they are defined in order to facilitate the systematic application of object-oriented data visualization. The process of data visualization must be addressed following the unfolding of the possibilities that arise from the combination of these factors, reaching the observed achievements at each crossroads between communication component x layer of organizational complexity see Figure 7.

Illustrative representation of the dimensional taxonomy for object-oriented data visualization from the perspective of communication sciences: elements-axes as factors of completeness and layers spheres as factors of complexity.

Source: Own elaboration. Previous theoretic and practical studies have led to the assumption that data visualization is mainly instrumental. Conversely, the results of this study reveal that the potentialities of the analytical functions of data visualization are strictly related to its ability to show the scale and the increasing intricacy of the networked organization of a complex system, in which relationships and processes are interconnected.

In other terms, the efficacy of data visualization not only depends on the completeness of its extended deployment taking into account communication factors but also on its in-depth unfolding following the level of organizational complexity in which the analysis has been performed. This holistic approach enables data visualization to be understood as the visual representation of knowledge, after data formalization and data analysis.

As the key time that culminates and completes data processing, data visualization summarizes the underlying background knowledge that potentially initiates a new inquiry in the innovation cycle. For an open discussion, it must be pointed out that the completion of data visualization, according to the proposed taxonomy, culminates data processing cycle, making visible the knowledge background. On this basis, scientific research, technological development, and transfer deploy the cycle of innovation Cavaller, , which, in turn, pushes data processing cycle for the extension of scientific knowledge see Figure 8.

So, in a major hyper-cycle, data processing and innovation cycles can be seen as an augmented projection of human cognitive process, where this taxonomy of data visualization can play an extended key role, an issue that constitutes the object for future research actions.

The author confirms being the sole contributor of this work and has approved it for publication. The author declares that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.

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Abstract This article consists of a conceptual analysis-from the perspective of communication sciences-of the relevant aspects that should be considered during operational steps in data visualization. The analysis is performed taking as a reference the components that integrate the communication framework theory-the message, the form, the encoder, the context, the channel, and the decoder-which correspond to six elements in the context of data visualization: content, graphic representation, encoding setup, graphic design and approach, media, and user.

This article was submitted to Research Assessment, a section of the journal Frontiers in Research Metrics and Analytics. Received Dec 18; Accepted Feb 9. The use, distribution or reproduction in other forums is permitted, provided the original author s and the copyright owner s are credited and that the original publication in this journal is cited, in accordance with accepted academic practice.

Keywords: index terms: data visualization, dimensional taxonomy, communication process, communication theory, knowledge transfer, complexity. Introduction Complexity as a Challenging Parameter to Integrate in Data Visualization Over the past decades, visualization and complexity have received extensive scientific attention, and there has been a huge increase in the number of publications dealing directly or indirectly with their relation.

The Lack of an Integral Data Visualization Taxonomy to Tackle Complexity Data visualization and complexity as scientific topics are undergoing a period of consolidation with an increasing and overwhelming number of scientific publications and specialists working on these fields.

Encoding Specifications and Configuration Settings The third node of the communication framework is the encoder, and its action is the encoding or the communication configuration. Data Formalization Adhoc. Setting up Data and Plotting Elements for Descriptive Visualization The first step in the basic configuration of data visualization is to specify and verify basic elements selected such as parameters, constants and variables, scale, data range, sample, legend, and labels, and to check them in preliminary views in order to manipulate and ensure the accuracy of the representation.

Multidimensional Transformation The configuration for the visualization of a multivariate set is basically solved in its transformation to a data matrix with rows and columns, representing cases and variables. Configuration of Data and Representation of Dynamic Multidimensional Distribution in Data Visualization: Integration of Applications The next step in the process of configuring data visualization focuses on the dynamic relationships and distributions between groups of variables, combining and communicating different visualization techniques and methodologies, which generates a fundamental requirement for dynamic, compatible, and interconnected tools for visual encoding.

Specifications for the Configuration of the Data and Its Graphical Representation in an Interactive Visualization Getting into the internal complexity of phenomena involves defining the different layers of sub- or super-processes that participate or overlap in strata, which in turn requires developing and mastering complex visualization tools. Display Settings for Visual Reconstruction In the comprehensive visual reconstruction of an organization, the convergence of data visualization and data analysis has become indispensable.

Graphic Design and Context: Modal Approaches and Properties of Good Data Visualization The fourth node of the communication framework is the context, which in data visualization is developed by graphic design. Subjective Approach Visualization must be meaningful.

Objective Approach Data visualization plays a critical role in multiple professional and academic fields, which means that it needs to adapt to particular specifications. Commercial Approach and Persuasive Communication In the commercial approach, the graphic designer does not only try to capture the attention and interest of the user but also tries to convince him of the benefits of a product and a service.

Accomplishment of Tasks and Efficiency The second level of the user experience in the use of data visualization occurs when the user is active and has an autonomous experience. Proficiency and Memorability A higher level of complexity in the requirements for good visualization based on the user experience is reached when the user is empowered by the acquired knowledge and expert mastery of the visualization tool. Feedback, Interaction, and Error Prevention: Supportiveness and Robustness The evaluation of the usability of data visualization tools can be carried out by studying the errors made by the user with the objective of introducing improvements for future prevention and for enhancing their robustness.

Results The results of the study conducted in this article can be classified into two groups: theoretical— which include a dimensional factors, b characterization of achievements—and practical , which include c types of data visualization, d functions, e principles of assessment, and f professional competences of data visualization. TABLE 2 Dimensional taxonomy of data visualization: factors of completeness and factors of complexity. TABLE 4 Variables, types of visualization, and graphical representation by goals from the perspective of an object-centered data visualization model Cavaller et al.

Content-variable Types of data visualization and graphical representation Object-goal 1 Measurements Descriptive Parameters and basic relationships 2 Indicators Relational Multivariate relationships 3 Distributions Multi-relational dynamics Factors or multi-relationships 4 Flow: vector Process Internal logics 5 Network: connector Hyper-process: system Architecture 6 Program: code Ecosystem Organization.

Author Contribution The author confirms being the sole contributor of this work and has approved it for publication. Conflict of Interest The author declares that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.

Editor Mortensen C. New Brunswick, New Jersey: Transaction. Social big data: recent achievements and new challenges. Fusion 28 , 45— Introduction to information visualization. Transforming data into meaningful information. The process of communication. Semiology of graphics.

London, United Kingdom: Esri Press. Visual representation». The encyclopedia of human-computer interaction 2a. InSense: interest-based life logging. Beyond memorability: visualization recognition and recall. IEEE Trans. Graph 22 1 , — Visual insights: a practical guide to making sense of data.

MIT Press. Atlas of knowledge: anyone can map. Graph 17 12 , — SCADA: supervisory control and data acquisition. Fourth Edition. Model-based clustering and visualization of navigation patterns on a web site. Data Mining Knowledge Discov. The Functional Art: an introduction to information graphics and visualization.

New Riders. The 8 types of graphic design. Card S. Readings in information visualization: using vision to think. San Francisco: Morgan Kaufmann Publishers.

Unlock the power of spatial analysis. CASModeling Complex adaptative systems modeling. Cavaller V. Matrix indicator system. Matrix indicator system for strategic information analysis: application in a practical case Dissertation. Cambridge forum for sustainability and the environment. Chen C. Science mapping: a systematic review of the literature. Data Inf. A survey of traffic data visualization.

Graphic design for scientists. Nanotechnol 10 , Chuang J. Termite: visualization techniques for assessing textual topic models. Proceedings of the international working conference on advanced visual.

May Cook D. Interactive and dynamic graphics for data analysis: with R and GGobi. Get in contact with us at webcelebs trinitymirror. See author’s posts. Save my name, email, and website in this browser for the next time I comment. Top Posts Stacey Solomon confesses birth happiness guilt after feeling Alec Baldwin armourer and assistant director who handed Mel C convinced Victoria Beckham will rejoin Spice Gun fired by Alec Baldwin in fatal shooting Giuliana Rancic Zendaya.

Geoffrey S. Publisher : Springer Singapore. Softcover ISBN : Series ISSN : Edition Number : 1. Skip to main content. Search SpringerLink Search. Buying options eBook EUR I have done the projects for both the B. Steph, thank you for all the help! I would like to thank Henk for being always present when I needed advice.

The person who has helped me the most during my PhD years — is my research supervisor, Dick Epema. I managed to tell Dick then that grid computing was a research area good only for republishing old research results, and that scheduling has no future, research-wise. An interesting discussion ensued, and, as it turns out, my PhD thesis is mainly about scheduling in grid computing. Dick, I was wrong, though my harsh statements can be matched with many examples from the published grid computing research of the past decade.

I have learnt a great deal about the professional side and even a little bit about the personal side of Dick Epema in these four years.

He gave me enough space to enjoy doing my research, encouraged me whenever he felt I was feeling low, and prevented me as much as possible from over-reaching. He contributed to my working environment being pleasant yet challenging. Most importantly, he helped a great deal in making our joint work really successful. Thank you very much for all the help, Dick Epema!

In the summer of , I started working on a grid performance evaluation tool, which later evolved into the GrenchMark framework Chapter 5. I was also helped for this work by the Ibis grid programming toolkit creators, Jason Maassen and Rob van Nieuwpoort. The ensuing discussions with Ramin Yahyapour, Carsten Ernemann, Alexander Papspyrou, and the rest of the group, led to a better understanding of the current needs for a grid performance evaluation framework.

Work in computer resource management is always dependent on realistic or real workloads, and for grids, which are some of the largest and most complicated computing systems to date, even more so. At almost the same time, I have also started to work with Javier Bustos-Jimenez on understanding the characteristics of volatile grid environments those environments set-up tempo- rary, for a multi-institute project.

I also want to thank the Condor team members, and especially Zach Miller, Matt Farrellee, and Tim Cartwright, for welcoming and working with me without reserves.

Condor rocks, no kidding. During the remaining two years of my PhD project duration I had the pleasure to collaborate with a wonderful group of people. In particular, I would like to thank Radu Prodan and Thomas Fahringer for their help, including facilitating my research visit to U.

Innsbruck in the summer of Thank you very much for taking the time to evaluate this long PhD thesis. My basketball team-mates have also been there at all times, since I met them tri-weekly in training and in low-level Dutch league games, and oftentimes in international basketball tournaments. Last, but certainly not least, I would not be here to tell this long story without constant support from my family.

Given that our daily activity rely on these services, it is surprising how little attention we give to the service provider. In a competitive market, the service providers tend to integrate into alliances that provide better service at lower cost. For example, while hundreds of airlines or various sizes exist e. Other day-to-day utilities, such as the telephone, water, and electricity, are similarly integrated and operated.

However, the computer, which is a newer daily utility, still lacks such integration. Computers are becoming more and more important for the well-being and the evolution of society. Over the past four decades, computers have permeated every aspect of our society, greatly contributing to the productivity growth1 [Oli00; Jor02; Pil02; Gom06].

The impact of computer-based information technology IT is especially important in the services industry and in research [Jor02; Ber03a; Tri06; i], arguably the most important pillars of the current U. Coupled with the adoption of computers, the growth of the Internet over the last decade has enabled millions of users to access information anytime and anywhere, and has transformed information sharing into a utility like any other.

However, an important category of users remained under-served: the users with large computational and storage requirements, e. Thus, in the mid-nineties, the vision of the Grid as a universal computing utility was formulated [Fos98]. While the universal Grid has yet to be developed, large-scale distributed computing infrastructures that provide their users with seamless and secured access to computing resources, individually called Grid parts or grids, have been built throughout the world, e.

The subject of this thesis is the inter-operation of grids, a necessary step towards building the Grid. Grid inter-operation raises numerous challenges that are usually not addressed in the existing grids, e.

We review these challenges as part of the problem of grid inter-operation in Section 1. New research challenges arise from the number and variety of existing grids, for example the lack of knowledge about the characteristics of grid workloads and resources, or the lack of tools for studying real and simulated grids.

We present these challenges in Section 1. In Section 1. However, the vast majority of these grids work in isolation, running counter to the very nature of grids. Two research questions arise: 1. How to inter-operate grids? What is the possible gain of inter-operating grids? Answering both questions is key to the vision of the Grid; we call these question the problem of grid inter-operation.

Without answering the second, the main technological alternatives to grids, large clusters and supercomputers, will remain the choice of industrial parties.

Resource Ownership The grids must be inter-operated without interfering with the ownership and the fair sharing of resources. Scalability The inter-operated grid must be scalable with respect to the number of users, jobs, and resources. Trust and Accounting The resource sharing in the inter-operated grid must be accountable and should involve only trusted parties. Reliability The inter-operated grid must attempt to mask the failure of any of its components.

Currently, there is no common solution to this problem. A central meta-scheduler is a performance bottleneck and a single point of failure, and leads to administrative issues in selecting the entity that will physically manage the centralized scheduler. A qualitative comparison is possible between the centralized architecture, which is the most-used architecture for large-scale cluster and supercomputer systems, and the architectures used for building grid environments.

Thus, we formulate in this section a third research question: 3. How to study grid inter-operation? We identify two main challenges in answering this question they are addressed in Chapters 3 and 4, and in Chapters 5 and 6, respectively : lack of knowledge about real grids, and lack of test and performance tools. Little is known about the behavior of grids, that is, we do not yet understand the characteristics of the grid resources and workloads.

Mostly because of access permissions, no grid workload traces are available to the community that needs them. Moreover, the simulation of grid environments is also hampered, as the several grid simulation packages that are available lack many of the needed features for large-scale simulations of inter-operated grids.

The framework comprises two main components: a toolbox for grid inter-operation research, and a method for the study of grid inter-operation mechanisms.

We describe these two components in turn. We describe each of the four tools in turn. Over the past two years, we have built the Grid Workloads Archive GWA , which is at the same time a workload data exchange and a meeting point for the grid community.

We have introduced a format for sharing grid workload information, and tools associated with this format. Using these tools, we have collected and analyzed data from nine well-known grid environments, with a total content of more than 2, users submitting more than 7 million jobs over a period of over 13 operational years, and with working environments spanning over sites and comprising over 10, resources. The GWA both content and tools has already been used in grid research studies and in practical areas: in grid resource management [Li07c; Li07e; Li07f; Ios06a; Ios07d], in grid design [Ios07c], in grid operation [Ios06b], and in grid maintenance [Ios07e; Str07].

We propose in our work a model for grid inter-operation that focuses on these two aspects. The resource change model characterizes both the short-term dynamics and the long-term evolution. The workload model takes into account individual jobs, job grouping e. The GrenchMark framework for testing large-scale distributed computing environments focuses on realistic testing, and on obtaining comparable testing results across platforms. To achieve this goal, we give special attention to realistic grid workload modeling, and to generating workloads that can run in real environments.

Our GrenchMark reference implementation addresses the practical problems of testing, and, in particular, of creating appropriate and repeatable experimental conditions in a large variety of environments. Over the past 18 months, we have used the reference implementation in over 25 testing scenarios in grids, in peer-to-peer systems, and in heterogeneous computing environments.

The current grid simulation environments still lack modeling features such as grid inter-operation, grid dynamics, and grid evolution, and research productivity features such as automated experiment setup and management.

We address these issues through the design and a reference implementation of DGSim, a framework for simulating grid resource management architectures. Thus, we propose a three-step method: 1. Identify relevant aspects for the design of grid inter-operation systems, which have to be validated by classifying real grid systems according to the relevant aspects; 2.

Assess qualitatively the grid inter-operation ability of the real grid systems according to the relevant aspects; 3. In our solution, the internal hierarchy of the grids is augmented with direct connections between nodes under the same administrative control, and the roots of the hierarchies are combined in a decentralized network. To operate this architecture, we employ the key concept of delegated matchmaking, in which resources that are unavailable locally are obtained through delegated matchmaking from remote sites, and added transparently and temporarily to the local environment.

We formulate the problem of grid inter-operation. We present a rationale and a formulation for this important grid research problem.

We propose a framework for the study of grid inter-operation mechanisms. Our framework comprises two main components: a toolbox for grid inter-operation research, and a method for the study of grid inter-operation mechanisms. The taxonomy focuses on two aspects: the architecture of the inter-operated grid, and the mechanism for operating the architecture.

We show how this taxonomy can be used to compare existing systems, and as a guideline for the design of new systems.

We design and validate a new grid inter-operation system. Our solution, Delegated MatchMaking, includes a hybrid grid inter-operation architecture and a new grid inter-operation mechanism. The chapters composing each part and the logical links between them are depicted in Figure 1. This chapter is based on material published in [Ios06b; Ios07c; Ios08e; Ios08g]. The requirements of a grid workloads archive are analyzed. The design and the main features of the GWA, and its past, current, and potential use are presented.

Finally, the workload archives used in computer science are surveyed and compared with the GWA. This chapter is based on material published in [Ios06a; Ios08d]. The chapter presents analysis results and models for grid resource dynamics and evolu- tion, and for grid workloads focusing on parallel jobs and on bags-of-tasks. This chapter is based on material published in [Ios06a; Ios07e; Ios07d; Ios08e].

The design goals of a testing framework for large-scale distributed computing systems are discussed. The resulting design and the main features of the GrenchMark framework, its key implementation details, its validation, and its past, current, and potential use are presented. Finally, existing testing approaches for distributed computing systems are surveyed and compared with the GrenchMark framework.

This chapter is based on material published in [Ios06b; Ios07a]. The requirements of a simulation framework for comparing grid resource management architectures are discussed. The design and the main features of the DGSim framework, its validation, and its past, current, and potential use are presented.

This chapter is based on material published in [Ios08f]. The practical limitations of the centralized grid inter- operation approaches are evaluated in a real environment.

The grid inter-operation ability of real grid systems according to the relevant aspects is assessed. This chapter is based on material published in [Ios07c; Ios08b; Ios08g]. This chapter is based on material published in [Ios07c; Ios08b]. Chapter 2 A Basic Grid Model 2.

Similarly, this thesis relies on the existence of a Grid system model. In contrast with these approaches, our key idea in building a Grid model is the design of a modular model, so as to accommodate a wide variety of scenarios instead of just one. We therefore split our Grid model into two parts: a basic part that deals with the common aspects of the Grid such as the system, the jobs, the users, and the job execution , and an extension part that includes modules for various more realistic aspects e.

The system, job, user, and job execution parts of the model are introduced in Sections 2. Last, in Section 2. Conference on Grid Computing Grid [Ios08g]. Each cluster is managed by a Cluster Resource Manager CRM , a middleware layer that provides an interface through which jobs are submitted for execution on the local resources. The CRM focuses on the management of a single set of resources, and is usually not capable of managing several distinct clusters.

On top of the CRM level i. A site is an administrative unit that combines all the elements with a common location, i. On each site, there is only one gateway, although the machine can be a multi-processor system more powerful than any other node in the cluster. The computing power of the gateway cannot be used for executing user jobs. For administrative purposes, a site may exist even when it lacks physical resources.

After submitting the jobs, the client may be disconnected from the server, and the server remains in the system to further represent the user and manage the received jobs for execution. Figure 2. None of these sites would be able to function in the absence of the inter-operation medium. We detail the components of the inter-operation medium, i.

Inter-Operation Medium Site-4 0. Site-5 – 20 0. Unitary jobs This category includes jobs that can be submitted as a single, unitary, description to the typical scheduler, e. Composite jobs This category includes jobs composed of several unitary jobs.

To distinguish be- tween the composite job and its components, we call the latter tasks. A bag-of-tasks job Figure 2. A chain-of-tasks job Figure 2. The dependency between two tasks determines when the dependent task can start. For simplicity, we consider only directed acyclic graphs DAGs.

We call root a node without predecessors and leaf a node without descendents; a DAG may have several roots and leaves. Often, the grid resource owner is tightly connected or even identical to a community of users, whose jobs it must prioritize over the jobs of any other user. Thus, for each resource owner there are two classes of users: the users that are tightly connected with that resource owner the local users and the other users the remote users.

Another common policy of the resource owners with respect to users is to limit the number of jobs they can simultaneously run on the resources of a single cluster, or even on the whole grid. Often, a VO will submit all its jobs through the same job manager, e.

Thus, the VOs share with their representative user the limitations imposed by the resource owners. After preparing the job input, the user submits the jobs to the user job manager. From the moment the user job manager acknowledges to the user that the jobs have been received, the jobs are considered to have been submitted to the Grid.

Boxes represent states, and continuous arrows represent information flows. The overhead components, i. However, this does not mean that the job must be executed on the CRM operating directly under that GRM; when many GRMs operate in the same Grid the job may be routed migrated before start between them. Once started, tasks run to completion, so we do not consider task preemption or task migration during execution.

Instead, tasks can be replicated and canceled, or migrated before they start. The step in the job execution model when the job moves to a GRM from the user job manager deserves more attention, as it involves much of the overhead associated with running jobs in grids we show in Section 7.

Then, its predecessor tasks i. From that point, the user job manager can begin executing task B. Table 2. Viewed through our model, DAS-1 was a grid with four sites, each comprising exactly one cluster there are no purely administrative sites. The DAS-1 system did not have any common-purpose grid middleware installed. Thus, the DAS-1 system was used mostly for research in programming models and systems software. The user job manager is still a set of command-line tools, but some composite jobs are now supported e.

Our approach for modeling a complex system such as the Grid is to create a modular model with two main parts: a basic part that deals with the common aspects of the Grid, and an extension part that includes modules for various complex aspects.

In this chapter we have presented the basic part of the Grid model used in this thesis, that is, the system, the jobs, the users, and the job execution. Chapter 3 The Grid Workloads Archive 3. Due to access permissions few grid workload traces are available to the community, which needs them.

The lack of grid workload traces hampers both researchers and practitioners. Most research in grid resource management is based on unre- alistic assumptions about the characteristics of the workloads. Most grid testing is in practice performed with unrealistic workloads, and as a result the middleware is optimized for the wrong use case, and often fails to deliver good service in real con- ditions [Ios06b; Kha06; Li06; Ios07b]. Thus, a fundamental questions remains unanswered: How are real grids used?

The goal of the GWA is to provide a virtual meeting place where the grid community can archive and exchange grid workload traces. Building a community fosters collaboration and quickens the permeation of ideas. Building a workloads archive makes the data available.

We have also drawn inspiration from a number of archival approaches from other computer science disciplines, e. We design the GWA around building a grid workload data repository, and establishing a community center around the archived data.

We further design a grid workload format for storing job-level information, and which allows extensions for higher-level information, e. We give special attention to non-expert users, and devise a mechanism for automated trace ranking and selection.

We have collected so far for the GWA traces from nine well-known grid environments, with a total content of more than users submitting more than 7 million jobs over a period of over 13 operational years, and with working environments spanning over sites comprising 10, resources. In Section 3. Then, we describe the design and the main features of the Grid Workloads Archive in Section 3.

Finally, in Section 3. Our motivation is twofold. We structure the requirements in two broad categories: requirements for building a grid workload data repository, and requirements for building a community center for scientists interested in the archived data.

Requirement 1: tools for collecting grid workloads. In many environments, obtaining workload data requires special acquisition techniques, i. Obtaining grid workloads data is comparatively easy: most grid middleware log all job-related events. Fourth, to provide uniformity, a workload archive provides a common format for data storage. The format must comprehensively cover current workload features, and also be extensible to accommodate future requirements.

To conclude, there is a need for tools that can collect and combine data from multiple sources, and store it in a common grid workload format requirement 1. Requirement 2: tools for grid workload processing.

Following the trend of Internet traces, sensitive information must not be disclosed. For grids, environment restrictions to data access are in place, so it is unlikely that truly sensitive data e. However, there still exists the need to anonymize any information that can lead to easily and uniquely identifying a machine, an application, or a user requirement 2a.

Time series analysis is the main technique to analyze workload data in computing environments. In addition, grids exhibit patterns of batch submission, and require that workload analysis is combined with monitoring information analysis. The data donors and the non- expert users expect a user-friendly presentation of the workload analysis data. The GWA community needs tools that facilitate the addition of new grid workloads, including a web summary.

Thus, there is a need for tools to create workload analysis reports requirement 2c. Requirement 3: tools for using grid workloads. The results of workload modeling research are often too complex for easy adoption. By comparison to previous computing environments e. There is a need for tools to extract for a given data set the values of the parameters of common models requirement 3a.

There is a need to generate synthetic workloads based on models representative for the data in the archive requirement 3b. Since the grid workload format can become complex, there exists also a need for developer support i. Requirement 4: tools for sharing grid workloads. Over time, the archive may grow to include tens to hundreds of traces.

There is a need for ranking and searching mechanisms of archived data requirement 4a. There is a need to comment on the structure and contents of the archive, and to discuss on various topics, in short, to create a medium for workload data exchange requirement 4b.

One of the main reasons for establishing the grid workloads archive is the lack of data access permission for a large majority of the community members. We set as a requirement the public and free access to data requirement 4c.

Requirement 5: community-building tools. There are several other community-building support requirements. There is a need for creating a bibliography on research on grid and related workloads requirement 5a , a bibliography on research and practice using the data in the archive requirement 5b , a list of tools that can use the data stored in the archive requirement 5c , and a list of projects and people that use grid workloads requirement 5d.

We discuss its design, detail three distinguish- ing features, and summarize its current contents. The non-expert user is the typical user of the archived data. This user type requires as much help as possible in selecting an appropriate trace. The expert user uses the archived data in an expert way.

Mainly, this user type requires detailed analysis reports and not automatic ranking, consults the related work, and may develop new analysis and modelling tools that extend the GWA data loading and analysis libraries. The GWA editor contributes to the community by commenting on the contents of the archive, and by adding related work.

The design requirements see Section 3. Figure 3. There is one module for collecting grid workload data. The Data Collection module receives grid workloads from the contributor or from the GWA Team, if the contributor delegates the task. There are several potential sources of data: grid resource managers e. The main tasks of the Data Collection module are to ensure that the received data can be parsed, to eliminate wrongly formatted parts of the trace, and to format data provenance information.

There are three modules for processing the acquired data. The Data Anonymization module anonymizes the content received from the Data Collection module, and outputs in the Grid Work- loads Archive format see Section 3. If the Contributor allows it, a one-to-one map between the anonymized and the original information is also saved.

This will allow future data to be added to the same trace, without losing identity correlations between added parts. The Workload Analysis module takes in the data from the Workloads Database, and outputs analysis data to the Workload Analy- sis Database. More details about this component are given in Section 3. The Workload Report module formats for the expert user the results of the workload analysis and sometimes of workload modeling.

The Workload Modeling module attempts to model the archived data, and outputs the results i. The input for this process is taken from the Workload Analysis Database input data , and from the Workload Models Database input models.

Several workload models are supported, including the Lublin-Feitelson model [Lub03]. The Workload Generator module generates synthetic grid workloads based on the results of the Workload Analysis and Modeling results, or on direct user input.

The third module not shown in Figure 3.

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Emergent complexity in systems theory is described as the distinctive novel properties or behaviors that arise in organizations from the interaction among their components Gibb et al. Adding complexity is the common response of organizations under the influence of controllable and uncontrollable factors, by means of which they adapt themselves to changes in the environment. In complex systems, emergent properties are provided by networks of internal processes and hyper-processes in order to accomplish a particular function, which means that there is a scale factor involved in their structure.

Complexity is deeply embedded in organizational dynamics, and it has become a real challenge for data visualization. If complexity characterizes in general any organization or phenomenon, by extension, the methods and techniques to visualize them must be accordingly modified or eventually adapted to capture the dimensional structure and scaled dynamics that configure the object. Among the fields in which publications about complexity have reached more popularity in the last few years, modeling of biological ecosystems May, , social complexity DeLanda, , self-organization in statistical mechanics Wolfram, , ecological complexity Allen and Starr, , and economic complexity Hausmann et al.

In a similar review focused on visualization, publications in cartography Kraak and Ormeling, , perception and design Ware, , sequencing technologies Katoh et al. In the particular field of data visualization, from its very beginning, pioneering works from authors such as Bertin , Tufte , Schneiderman , Horn , and Wilkinson , followed by precursors such as Fayyad et al. Data visualization and complexity as scientific topics are undergoing a period of consolidation with an increasing and overwhelming number of scientific publications and specialists working on these fields.

However, along with this positive impression, a more detailed overview suggests that linked problems remain unsolved:. The root cause of the above-mentioned problems is the absence of an operative standard for the implementation of data visualization.

As a consequence, the main deficit, repeatedly observed throughout this review, is that data visualization is still affected by a serious lack of systematicity which ultimately—from the perspective of communication sciences—can be summarized as the lack of an integral taxonomy. There is no science without its own taxonomy. Taxonomy is the practice used by any science to clarify itself by classifying its concepts, being thus an exercise of self-explanation about its fundamentals.

Data visualization occupies a central position as an applied science—in an intersection among statistics, semiotics, computer science, graphical design, and psychology, in close relation to communication sciences—which means that the meta-analysis required in order to generate a taxonomy must be performed over multiple scientific disciplines.

Being central paradoxically represents a weakness. Despite the fact that there have been tentative approaches to define a taxonomy in particular areas of data visualization Schneiderman, ; Heer and Shneiderman, ; Ruys, , the critical requirement for an integral taxonomy is a pending workload, and it is currently having a negative impact on both its consolidation as a rigorous technical method and on its recognition as a scientific discipline, beyond its instrumental use.

Faced with this situation, it is appropriate to shed light on the foundations of the discipline of data visualization—understood as a communication process—in order to provide a solid ground for its systematic application.

To achieve such purpose, a key action is required. Complexity has to be integrated as an internal parameter in the configuration of its operative. As complexity is a factor that constitutes the object and conditions the subject, data visualization needs to undergo a conceptual analysis object-centered on organizational complexity, which in turn must be tracked to each of the components of the communication process that participates in data visualization.

This article is focused on this objective. Any scientific research inquiry follows three procedural stages when managing data: data formalization, data analysis, and data visualization, which, respectively, transform observations and measurements into data, data into information, and information into knowledge. Formal data appear as a result of preprocessing operations, information appears as a result of data analysis, and knowledge appears as a result of data visualization.

Data visualization can be transversely used as a tool in both processes of data formalization and data analysis, but ultimately, it constitutes the final and synthetic visible stage where the results of data analysis are reported.

In fact, by means of the accuracy of data visualization, the success of any data processing is evaluated. In order to provide instruments from communication sciences that can contribute to the process of transforming data into understandable information and information into valid knowledge, it is necessary to deal with data visualization in a systematic way covering the totality of the factors that are involved in its process.

The first step to start a thorough review of these factors is to identify the following elements that participate in data visualization understood as a communication process:. The proposal of these elements is not arbitrary. However, from the point of view of the communication theory, these core elements are embedded in data visualization, beyond its background and application, in so far as they correspond to the most widely accepted framework of the communication model Shannon and Weaver, ; Schram, ; Berlo, ; Rothwell, ; Barnlund, The elements are as follows:.

These six elements must be considered as factors of completeness in data visualization. The failure to observe any of them is a recurring cause of miscommunication and misunderstanding. Data visualization constitutes a process of communication, the efficiency of which is conditioned by the actions that these elements imply: the selection of the content, the formal representation of the information, the encoding and setup of the visualization, the graphical design appropriate to the context, the adaptation to the medium, and the observation of user preferences.

Which functionalities from which tools are appropriate for the graphical representation to be integrated in the pursued channel? What properties does the visualization have to meet depending on the target or audience? What are the levels of communication efficiency that must be achieved? The above questions highlight six dimensions of the communication process that, conditioning the systematic procedure of data visualization, must be accurately studied:. The definition of these dimensions leads to the equally important issue of internal order in which they must be unfolded.

Consequently, the sequential and detailed unfolding of data visualization—covering degrees, functionalities, specifications, modes and properties, levels, and requirements—must be internally described through cross-cutting layers. Taking this conception as a starting point of the review and the analysis, the goal of this article was to design an object-centered data visualization model , organized in two axes:.

As a result, a dimensional taxonomy of data visualization based on a matrix structure—where the elements that participate in data visualization act as factors of completeness, and their development in layered dimensions act as factors of complexity—is proposed see Table 1. Matrix architecture of factors of completeness and complexity for the design of the dimensional taxonomy of data visualization according to the components of the communication framework theory.

It must be observed that building the proposed taxonomy, the theoretical framework of communication sciences is projected as the practical framework for the dimensional analysis of data visualization.

Meaning that in order to validate it, this article has been focused on an extensive systematic review of the scientific literature and on a conceptual analysis about the relevant aspects that have been considered both in practice and in the current debates about data visualization, categorizing them into topical groups taking as a reference those components and layers. The first node of the communication framework is the message or the content of the communication. The first of the main functions of data visualization is to communicate a message: generally, information about an event, a phenomenon, a process, a system, or in general, any observable subset of the real world.

At this starting stage, the assumption of the quality of data about the object is accepted as a fact because it should result from previous tasks of data formalization and analysis. Data visualization, from the perspective of the content to be represented, must distinguish six degrees of abstraction of information which correspond to six layers of organizational complexity. In practical terms, data visualization can be faced with three potential initial scenarios: a requirement of data visualization without previous data formalization, without previous data analysis, or, in the best case, with both data formalization and analysis previously performed.

In the first scenario—that could be called agile, adhoc , or express demand—data visualization procedure must introduce a delay to examine the target in detail, to seek evidence, and to detect the different properties which presumably can be sustained by available data, in order to complete a proper answer to the requirement. The so-called data wrangling or data preprocessing operations are required before data analysis; such operations include data cleaning, matching, organization, and aggregation Chen et al.

In the second scenario, once a formalized dataset has been obtained or is available from a system of information, the actions to be carried out can directly jump to check whether the target can be delimited and whether a reduced and representative sample for a deeper analysis is available.

In the third scenario, as the attention has already been focused on the particular issue, the consequent step is to select the data and constitutive relations that adequately answer the visualization requirement. In any case, evidence must exist and must be reducible to parameters and measurable.

The congruence as the essential quality of being in agreement with the real-observed facts should be the principal and basic characteristic of data visualization. A second degree of abstraction of information is reached when the requirement for data visualization needs adding and accumulating new observed properties about the subject to the focus. The process of aggregating variables describing parametrical relations needs a thorough investigation, comprehensive in scope.

A formal condition of this clustering can be defined as exhaustivity , the need to address all aspects without omission. The next degree of abstraction of the information is focused on the dynamics which refers to the multiple and observable distributions and relationships between sets of variables. It is understood that prior to data visualization, data analysis has been carried out in terms of detecting correlation or causality between variables.

The definition of the relationships, as patterns in the dynamics, between sets of variables is considered as explanation of the variations observed in the phenomenon. A pattern is defined as any regularly repeated arrangement or relation in or between a set of parameters that modifies others or changes itself according to its distribution.

Among all reasonable explanations, the best one covers the greatest spectrum of observed relationships or fits well enough to a sufficient portion of all the available information. The consistency is the modal quality—of being in the harmony, compatibility, and uniformity—that the explanation with the observation of particular distributions should pursue when dealing with the content of data visualization.

In case of wanting to visualize a complex phenomenon, usually associated to a process, the definition of the parameters, the construction of indicators, or the detection of interconnected factors or patterns is not enough because the abstraction required is an, more than probable, explanation.

Explaining a phenomenon as a set of separated dynamics is not sufficient either. The fourth degree of information abstraction involves the conceptualization of the internal relationship, the sequential process, and the vector direction that describes a phenomenon or lies behind the events. The nature of the interconnection between the dimensions of a process has to be observed as an objective condition of having a logical unity in coherence.

When an explanatory model is involved as a communication message, data visualization requires a previous conceptualization, summarizing the accepted premises about the object logically interconnected. When considering systems where hyper-processes—resulting from the coexistence of interconnected processes—are involved, a higher degree of abstraction in information must be achieved.

The internal complexity of a phenomenon needs the definition of the layers where each constituent process takes place. The object of data visualization at this level goes from what was initially perceived as an isolated process to its interaction with other processes that condition each other, defining a network of system functions and their interactions. Figure 1 shows the graphical representation of data on the layers of parallel activities undertaken by a university, illustrating how they participate in scientific research and technological development.

The multilayered structure describing a hyper-process model is a clear expression of the crucial ability of systems to adapt to the complexity of the changing environment. Detail of the interactive diagram map of the evolution of projects, patents and publications, considered as layers of parallel outcome processes of UPC in the period — Source: Cavaller et al.

Scientific progress implies the proposal of competing explanatory models, the certainty of which cannot be achieved. So there being no verifiability but falsifiability by experimentation Popper, , the evaluation of the confirmatory or falsifying value of evidence about a hypothesis depends on their demonstrative condition, which data visualization must facilitate in order to achieve scientific consensus.

The degree of abstraction of the information is correlated with the complexity of the entity from which data have been obtained and data visualization has to show. The procedure of grouping a network of interactive processes in different layers is definitely dealing with the highest level of complexity that culminates the scope of data visualization in which an organization within its environment is explained.

Scientific modeling and simulation are the results of a simplification and abstraction of human perception and conceptualization of reality that in turn come from physical and cognitive constraints. Modeling allows scientists to implement their reconstruction , simulating the program or code of the organization, future behaviors, visualizing scenarios, manipulating, and gaining intuition about the entities, phenomena, or processes being represented, for managerial or technical decision-making.

At this level, uncertainty is a transcendent condition characterized by limited knowledge which ranges just beyond the experimentation in order to achieve a holistic view of a phenomenon. Once the answer to the ominous question—which data in which degree of abstraction related to which level of organizational complexity about which object is required to be represented—is clear, the next question is: What is the ideal graphic representation to visually transform these data with a strictly functional orientation?

This decision is not trivial. Principles of graphic communication, studied by semiology or semiotics, under which diagrams, networks, and maps or any sign in general are used, have been designed for the production of meaning in their close relation to the analysis of the information that they represent Bertin, Here, it is worth remembering that one of the most recurrent errors in data visualization is to confuse the criteria for the selection of a proper graphic representation of data with the criteria of the graphic design of the visualization.

The graphic representation from a functional point of view is directly related to the nature of the content to be displayed. In this sense, six different object-oriented graphic representations with six different functionalities can be defined. The basic functionalities in the graphical representation of data are associated with a descriptive visualization of the parameters that depict a phenomenon. The development and formalization of statistical tools for the analysis and graphical representation of data have had a great impact in the field of visualization Friendly, Graphs are useful to show relationships among variables—how a whole is divided into different parts, how variables have changed over time and their range, when and how data are connected, what are the trends, and how changes in one variable affect another—or to obtain a sequence in the development and transformation of trends or patterns.

The main quality that is required from a graphic representation is to be descriptive. In this sense, the evidentiality , the condition to provide evidence, in an illustrative, expressive, and depictive way, is an essential condition by means of which the quality of the graphical representation in data visualization is evaluated.

Multivariate or relational visualization involves the observation of multiple measurements and their relationship. There are different methods of visualizing a multidimensional or multivariate reality capable of covering a wide spectrum of inputs and outputs, associated with different analysis techniques and methodologies.

In general, the need to express comparison, correlation, distribution, proportions, and hierarchy relationships in a dataset requires advanced functionalities in the visualization design.

Two of the main principles of graphical integrity defined by Edward Tufte are referred to as proportionality and disambiguitty. The property that pursues advanced forms of graphic representation is integrity , the formal condition of maintaining a direct proportion in the scale relationship of the parts with the whole and with the unit of measurement, without distorting the degree of interdependence of the variables. Dynamic or multi-relational visualization represents a reality where all the factors—defined as set of related parameters—are interconnected, and therefore there is interdependence between them, and consequently, their network position changes according to a spatial or temporal joint distribution.

A dynamic visualization of data has to be facilitated by functionalities of tools for the transmission and understanding of the global and interconnected networked nature of a reality that is itself dynamic.

Modeling of dynamic interaction networks has traditionally been supported by graph stream techniques or dynamic graph models Harary, Process visualization must describe the internal logics that lie behind phenomena.

Once the interdependence relationships between the different factors or dimensions of a phenomenon are known, the existence of its internal logic can be inferred, and therefore it is possible to define an explanatory model and proceed to its visualization.

However, in order to obtain a synthetic visualization that brings together the different dynamic perspectives of the same reality, continuing with a quantitative gradation in the abstraction of information and with its corresponding visualization is meaningless or clearly insufficient.

The natural path to parameterization and visualization requires a qualitative leap that is made through symbolic abstraction with the use of info graphic representation and animation techniques Harrison et al.

The graphic representation at this level of complexity is done through process graphs, graph processing workflows, info graphics, and motion graphics Curcin et al. The quality of the process of graphical representation is defined by its objective condition of expressing the logic of their transformation flow, which refers to its sequential or flow logicality. In Figure 2 , a process graph describing the Sextuple Helix Model for the assessment of universities based on KT processes is shown.

The activities—as nodes—are proposed, in a two-way cyclical sequence, for their correspondent accounting and mission values Cavaller, Hyper-process or system graphical representation is needed to observe the constituent layers when describing the architecture of the systems where different processes coexist. The graphic representation of the data at this level must allow the user to interact with the visualization in order to know independently or together the different layers that are integrated into the phenomenon and their connection.

One of the most common forms of graphic representation for interactive visualization is the interactive map. In general, interactive graphics are a type of graphic representation, which points to a demonstrative condition of an explanatory model, the ability to show evidence that verifies or refutes a hypothesis or theory that is defended. This property of complex evidentiality referred to a graphical representation means that its quality is evaluated by a demonstrative condition, the ability to give detailed, interactive, and ad hoc access to evidence of complexity, in order to demonstrate all the factors of a theory that is defended.

Multidimensional phenomena structured in different layers of processes, where different organizational systems are involved, make their graphic representation an extremely complex matter. This difficulty has led to the need to develop new functionalities in the visualization tools that allow a comprehensive and holistic representation.

At this level of complexity, the convergence between the symbolic and the analytical paths in data visualization prevails and requires that the graphic representation of data be accompanied in turn by a figurative or symbolic visual reconstruction of the reality of the phenomenon.

This requirement is easily observable in scientific visualization, such as in modeling projects of biological systems Ambicon, or in the field of medicine Jang et al.

The quality of a scientific visualization is evaluated by the capacity of reconstitution of reality in its entirety, even in what is not known in detail. The graphic representation associated with scientific modeling and simulation is characterized by the ability to represent the intricacy of phenomena internally and in their relationship with their environment, reconstructing those elements in which no sufficient evidence is available and posing them for a future demonstration.

The third node of the communication framework is the encoder, and its action is the encoding or the communication configuration. The main function of data visualization associated with this node is to communicate, so as to add meaning to the data and transform it into information.

The first step in the basic configuration of data visualization is to specify and verify basic elements selected such as parameters, constants and variables, scale, data range, sample, legend, and labels, and to check them in preliminary views in order to manipulate and ensure the accuracy of the representation. The basic operations to be performed in this phase have been proposed—in a synthetic way—as tasks grouped into three high-level categories: 1 specification of data and views visualize, filter, order, and derive , 2 view manipulation select, navigate, coordinate, and organize , and 3 process of analysis and provenance record, note, share, and guide Heer and Shneiderman, The specifications for a basic encoding of the data and its graphical representation pursue accuracy , an essential quality of being correct or precise for a basic visualization.

The configuration for the visualization of a multivariate set is basically solved in its transformation to a data matrix with rows and columns, representing cases and variables. There are different theoretical approaches or models that describe the procedural stages of configuring data visualization. Multidimensional transformation is related to the concept of visual metaphors and to the capacity for interaction Kosara et al.

The specifications for the configuration of multidimensional data and its graphical visualization pursue the preservation and detailed rigor of the proportions in the relationships detected between variables Blackwell, ; GIS, In multidimensional data representation, such as 3D scatterplots, this is done by using a software volume renderer for display, combining it with InfoVis interaction methods such as linking and brushing Kosara et al. The property or quality that is pursued in a multidimensional configuration of data visualization is multidirectionality , a formal condition, which is defined as the ability to show the widest possible range of interrelationships between set of variables.

The next step in the process of configuring data visualization focuses on the dynamic relationships and distributions between groups of variables, combining and communicating different visualization techniques and methodologies, which generates a fundamental requirement for dynamic, compatible, and interconnected tools for visual encoding.

New tools have been designed to facilitate application integration in visualization design. One successful example of this capability is dashboards, very useful embedded tools that allow the programmer to develop visualizations of known variables, dimensions, and relationships from a dataset Klipfolio, The difficulties in detecting and making the behavior patterns of dynamic distribution visible are associated with the difficulties of integration of the visualization tools Heer and Agrawala, The modal quality that is required from the configuration of a dynamic visualization is its versatility in terms of interconnectivity and compatibility with other tools.

Following consecutive levels of complexity, the configuration is directed to the design and programming of algorithms that simulate the operation of the logical structure of the process that underlies the phenomenon to be represented graphically. The modeling of a process, in order to visualize it, includes different operational moments: 1 defining the flow diagrams and the forms of representation, 2 selecting inputs and outputs of the processes for each of the events and activities, and 3 obtaining or designing the algorithms that synthetically define their relationship in the analyzed process.

The configuration has to point to the definition of an explanatory model that is represented and, therefore, to the logical structure that underlies Curcin et al. An example of a software tool that allows the configuration of process visualizations by generating algorithmic art is processing Reas and Fry, ; Terzidis, ; Greenberg et al.

In machine industry and manufacturing methods, control systems such as the Supervisory control and data acquisition SCADA incorporate graphical user interface GUI and allow users to interact with electronic devices, computers, networked data communications through graphical icons, and audio indicator Boyer, ; Siemens, The property that is sought in a configuration of the visualization of a process is being self-explanatory , an objective condition of being able to express the autonomous mechanics of a process easily understood.

Getting into the internal complexity of phenomena involves defining the different layers of sub- or super-processes that participate or overlap in strata, which in turn requires developing and mastering complex visualization tools. The specifications for the configuration of an interactive visualization are framed in the experimental and demonstrative stages of the research. In the comprehensive visual reconstruction of an organization, the convergence of data visualization and data analysis has become indispensable.

The goal is to provide—in an interactive way—simultaneous calculation and visualization of the interconnected relationships among variables, distributions, and flow of processes in the different layers and phases of systems in organizations. Visual analysis, modeling, and simulation of ecosystems and organizations are quite common, especially in the field of topological data analysis Xu et al.

Complex adaptive systems modeling can be found in a wide range of areas from life sciences to networks and environments CASModeling, Analysis and visualization of large networks can be performed with program packages, such as Pajek Mrvar and Batagelj, The property that the configuration of an integrative visualization has to pursue is the ubiquity in order to accomplish a synthetic and holistic vision and analysis, which can be characterized as the capacity of understanding the complexity of a system by making it visible.

The final step in the encoding of data visualization reaches the definition of the cross-layers of the functional system, which means to visually configure the vertical interconnection between the processes at their different layers. Figure 3 shows a representation of the multilayered innovation ecosystem that involves science, technology, and business sub-ecosystems as an example of cross-layer analysis of collaborative network to investigate innovation capacities Xu et al.

Example of cross-layer analysis and visualization of a collaborative network in a science—technology—business ecosystem. Source: Xu et al. The fourth node of the communication framework is the context, which in data visualization is developed by graphic design. The effectiveness of the design of data visualization is evaluated by its impact on the user, and it is explained by the mechanisms of human perception of esthetic forms in particular contexts.

The context is the criterion that classifies the approach modes to visualization and the esthetic forms of graphic design adopted. Visualization must be meaningful. It has to pursue the properties of any communication act—clarity, concreteness, saving time, stimulating imagination and reflection, empowering the user, etc.

In the subjective approach, the idea of context in its association with graphic design has to be defined considering the human—computer interaction HCI. The principles of visual representation for screen design and the basic elements or resources used such as typography and text, maps and graphs, schematic drawings, pictures, node-and-link diagrams, icons and symbols, and visual metaphors should be observed. Engelhardt in his analysis of syntax and meaning in maps, charts, and diagrams establishes a classification of the correspondence systems between design uses and graphic resources Blackwell, Complementing the coding that the brain automatically performs, the design can be used for recontextualization.

The property that data visualization pursues through its graphic design in a subjective approach is communicativity , an essential condition or quality of being able to convey meanings from one entity or group to another through the use of mutually understood signs, symbols, and semiotic rules. Geoffrey S. Publisher : Springer Singapore. Softcover ISBN : Series ISSN : Edition Number : 1.

Skip to main content. Search SpringerLink Search. Buying options eBook EUR The Data Anonymization module anonymizes the content received from the Data Collection module, and outputs in the Grid Work- loads Archive format see Section 3. If the Contributor allows it, a one-to-one map between the anonymized and the original information is also saved.

The Workload Generator module generates synthetic grid workloads based on the results of the Workload Analysis and Modeling results, or on direct user input. The third module not shown in Figure 3. The GWA contains three modules for data sharing. This process is further detailed in Section 3. To enable quick processing, the data is stored as raw text and as a relational database. This module has a web interface to allow the public and free distribution of the data within.

The Grid Workload Archive contains various additional community-building support, e. There are two design aspects to take into account. First, there are many aspects that may be recorded, e. Second, grid workload data owners are reluctant to provide data for a format they have not yet approved. Thus, one must provide the simplest possible format for the common user, while designing the format to be extensible. To further ease the adoption of our format, and as a step towards compatibility with related archives, we base it on the PWA workload format SWF, the de-facto standard format for the parallel production environments community [ Th07].

From a practical perspective, the format is implemented both as an SQL-compatible database GWF-SQLite , which is useful for most common tasks, and as a text-based version, easy to parse for custom tasks.

Since grids are dynamic systems, using the workload data in lack of additional information e. To address this issue, we have already designed and used a minimal format for resource availability and state [Ios07e].

We design a trace mechanisms that ranks traces and then selects the most suitable of them, based on the requirements of the experimental scenario. Users 0. We denote by Ws a hypothetical workload that has sample-like characteristics, i. We denote by C0 the set of all six categories. Consider an experimental scenario in which only some of the categories from C0 are relevant for workload selection, e.

Let C be the subset of C0 that includes only the categories relevant for the scenario at hand; we call such C a scenario-dependent subset of C0. The denominator in Equation 3. The ranking and selection mechanisms use the distance between workloads. We present online the ranking table that uses the scenario-independent value of the traces present in the GWA. Note that several are under processing, or have pending publication rights.

The data sources for these traces range from local resource managers e. In several cases, incomplete data is provided, e. Jobs can be submitted directly to the local resource managers i. To achieve low wait time for interactive jobs, the DAS system is intentionally left as free as possible by its users. The traces collected from the DAS include applications from the areas of physics, robotics, graphics, v-environments, CAS, AI, math, chemistry, climate, etc. In addition, the DAS traces include experimental applications for parallel and distributed systems research.

In addition, the Grid traces include experimental applications for parallel and distributed systems research. In these logs, the information concerning the grid jobs is logged locally, then transferred to a central database voluntarily. The logging service can be considered fully operational only since mid We have obtained traces from the four dedicated computing clusters present in NGS. The LCG production Grid currently has approximately active sites with around 30, CPUs and 3 petabytes storage, which is primarily used for high energy physics HEP data processing.

There are also jobs from biomedical sciences running on this Grid. Almost all the jobs are independent computationally-intensive tasks, requiring one CPU to process a certain amount of data. This Condor-based pool consists of over machines shared temporarily by their rightful owners [Tha05a]. The trace spans four months, from September to January The GWA-T-8 trace is extracted from the Grid3, which represents a multi-virtual organization environment that sustains production level services required by various physics experiments.

The infrastructure was composed of more than 30 sites and CPUs; the participating sites were the main resource providers under various conditions [Fos04]. These traces capture the execution of workloads of physics working groups: a single job can run for up to a few days, and the workloads can be characterized as directed acyclic graphs DAGs [Mam05].

The traces collected from Grid3 include only HEP applications. In the analyzed traces, workloads are composed of applications targeting high-resolution rendering and remote visu- alization; ParaView, a multi-platform application for visualizing large data sets [06], is the commonly used application. The busiest month may be different for each system. The toolbox provides the contributors and the expert users with information about the stored work- loads, and can be used as a source for building additional workload-related tools.

The workload analysis focuses on three aspects: system-wide characteristics e. Note the log scale for time-related characteristics. Figures 3. To ease the comparison, the bottom sub-graph depicts the hourly system utilization during the busiest month, for each environment. The system utilization is not stable over time: for all studied traces there are bursts of high utilization use between periods of low utilization.

We believe that this corresponds to the real use of many grids for the following reasons. Second, there are few parallel applications in the GWA traces relative to single-processor jobs. Jobs 1. Only the top 10 users are displayed.

The vertical axis shows the cumulated values, and the breakdown per week. For each system, users have the same identifiers labels in the left and right sub-graphs. Third, for the periods covered by the traces, there is a lack of deployed mechanisms for parallel jobs, e.

Co-allocation mechanisms were available only in the DAS, and, later, in Grid Even with the introduction of co-allocation based on advance reservation in several of the other grids i. Jobs production production LCG 2. Special events such as middleware change are marked with dotted lines.

The production period is also emphasized. The results depicted in Figures 3. However, some of the systems were not in production from the beginning to the end of the period for which the traces were collected. Moreover, the middleware used by a grid may have been changed e. We observe four main trends related to the rate of growth for the cumulative number of submitted jobs the input. Third, the period before entering production exhibits a low input i.

In this section, we discuss the use of the GWA in three broad scenarios: research in grid resource management, for grid maintenance and operation, and for grid design, procurement, and performance evaluation.

We have already used the archived content to understand how real grids operate today, to build realistic grid workload models, and as real input for a variety of resource management theory e.

The study in [Ios06a] shows how several real grids operate today. The authors analyze four grid traces from the GWA, with a focus on virtual organizations, on users, and on individual jobs charac- teristics. They further quantify the evolution and the performance of the Grid systems from which the traces originate. The imbalance of job arrivals in multi-cluster grids has been assessed using traces from the GWA in another study [Ios07c].

Hui Li et al. This gives evidence that realistic workload modeling is necessary to enable dependable grid scheduling studies. Finally, the traces have been used to show that grids can be treated as dynamic systems with quantifyable [Ios06d] or predictable behavior [Li04; Li07f]. These studies show evidence that grids are capable to become a predictable, high-throughput computation utility. The contents of the GWA has also been used to evaluate the performance of various scheduling policies, both in real [Ios06b] and simulated [Ios06d; Ios07c; Li07b] environments.

Finally, the tools in the GWA have been used to provide an analysis back-end to a grid simulation environment [Ios07c]. We detail below two such cases. A system administrator can compare the performance of a working grid system with that of similar systems by comparing performance data extracted from their traces. Additionally, the performance comparison over time e. In large grids, realistic functionality checks must occur daily or even hourly, to prevent that jobs are assigned to failing resources.

Our results using data from the GWA show that the performance of a grid system can rise when availability is taken into consideration, and that human administration of availability change information may result in times more job failures than for an automated solution, even for a lowly utilized system [Ios07e]. Similarly, functionality and stress testing are required for long-term maintenance.

The grid designer needs to select from a multitude of middleware packages, e. Or 50 times, or times Using workloads from the GWA, and a workload submission tool such as GrenchMark, the designer can answer these questions for a variety of potential user workloads. During the procurement phase, a prospective grid user may select between several infrastructure alternatives: to rent compute time on an on-demand platform, to rent or to build a parallel production environment e.

Similarly to system design and procurement, performance evaluation can use content from the GWA in a variety of scenarios, e. Parallel Production Environments: processing time consumed by users, and highest number of jobs running in the system during a day.

Note that the same approach may be used during procurement to compare systems using trace-based grid benchmarking. We target courses that teach the use of grids, large-scale distributed computer systems simulation, and computer data analysis. The reports included in the GWA may be used to better illustrate concepts related to grid resource management, such as resource utilization, job wait time and slowdown, etc. The tools may be used to build new analysis and simulation tools.

The data included in the archive may be used as input for demonstrative tools, or as material for student assignments. We assess the relative merits of the surveyed approaches according to the requirements described in Section 3. By the beginning of s, this shift in practice had become commonplace [Jai91; Cal93]. The Internet community has since cre- ated several other archives, i. These archives have gradually evolved towards covering most of the requirements expressed in Section 3.

Contrary to the Internet community, the computer systems communities are still far from ad- dressing Section 3. Since then, several other archives have started, e. For the cluster- based communities, the Parallel Workloads Archive PWA covers many of the requirements, and has become the de-facto standard for the parallel production environments community.

Recently, the PWA has added several grid traces to its content. The lack of grid workloads hampers the research on grid resource management, and the practice in grids design, management, and operation. To collect grid workloads and to make them available to this diverse community, we have designed and developed the Grid Workloads Archive.

The design focuses on two broad requirements: building a grid workload data repository, and building a community center around the archived data. For the former, we provide tools for collecting, processing, and using the data. For the latter, we provide mechanisms for sharing the data and other community-building support.

We have collected so far traces from nine well-known grid environments. For the future, we plan to bring the community of resource management in large-scale distributed computing systems closer to the Grid Workloads Archive. In Chapter 2 we have introduced a basic model for multi-cluster grids, covering the resource types, the job types, the user types, and the job execution model. However, two time-varying aspects of multi-clusters grid were not covered by the basic model: the resource availability, and the system workload.

In a multi-cluster grid, all the resources of a cluster may be shared with the grid only for limited periods of time, e. Fur- thermore, grids experience the problems of any large-scale computing environment, and in addition are operated with relatively immature middleware, which increases further the resource unavailability rate.

Grid resources are dynamic in both number and performance. We identify two types of change: over the short term e.

We call the former type of change grid dynamics, and the latter grid evolution. Disregarding grid dynamics during grid design may lead to a solution with low reliability. Disregarding the grid evolution may lead to a solution that does not match the systems of the future.

While many studies cover the resource availability in computing systems that are related to multi-cluster grids i. Thus, an important question arises: What are the characteristics of the resource un availability in multi-cluster grids? At one extreme, grid researchers have argued that grids will be the natural replacement of tightly coupled high-performance computing systems, and therefore will take over their highly parallel workloads [Ern02; Ern04; Ios06c; Ran08a].

At the other extreme stand arguments that grids are mostly useful for running conveniently parallel applications, that is, large bags of identical instances of the same single-node application [Tha05a]. The lack of information about the characteristics of grid workloads hampers the testing and the tuning of existing grids, and the study and evolution of new grid resource management solutions, e.

Without proper testing workloads, grids may fail when facing high loads or border cases of workload characteristics. Without detailed workload knowledge, tuning lacks focus and leads to under-performing solutions.

Thus, an important question arises: What are the characteristics of grid workloads? The model design component presents the elements of the model. The data analysis component determines for each model element the statistical properties of the real data. The modeling component leads to the selection of parameter values for the model elements. We conclude each answer with the description of a generative process that uses the model to generate synthetic data for simulation and testing purposes.

In Section 4. Several other studies characterize or model the availability of environments such as super- and multi-computers [Tan93], clusters of computers [Ach97; Zha04; Fu07], meta-computers computers connected by a wide-area network, e. Due the collection in the Grid Workloads Archive see Chapter 3 of workload traces from pro- duction grids, it is now possible to study the characteristics of grid workloads, and answer the second research question from the previous section.

The analysis tools of the Grid Workloads Archive have revealed that in contrast to the workloads of tightly coupled high-performance computing systems, a large part of the workloads submitted to grids consists of large numbers of single-processor tasks possibly inter-related as parts of bags-of-tasks.

Thus, the key idea of our attempt is to focus on bags-of-tasks, but to also model the parallel jobs that exist in grid workloads. For the case when the group size is one that is, the jobs arrive independently , we adapt to grids the Lublin-Feitelson workload model [Lub03], which is the de-facto standard workload model for parallel production environments Section 4.

The seminal work in grid workload modeling by Hui Li has demonstrated that long-range depen- dence and self-similarity appear in grid workloads [Li07f]. We have also shown in our previous work that job arrivals are often bursty [Ios06a] see also Figure 3.

While being the most common models employed in computer science, and in particular in queuing theory and its numerous applications [Kle75; Kle76], the Poisson models cannot model self-similar arrival processes [Lel94; Pax95; Err02]. The main advantage of our workload model over Poisson models is its potential ability to generate the self-similarity observed in grid environments i. We start with an introduction to the modeling process followed in this chapter in Section 4.

Then, in Sections 4. Finally, in Section 4. This section presents the method we follow to answer the research questions formulated in Section 4. The goal of modeling is to create a representation of the real data that is as close as possible to the original. These values are later used in real-world experiments or in simulations. The use of a well-known distribution facilitates in addition mathematical analysis, thus enabling the comparison of real or simulated experiments with theoretical results.

An important quality of a model is its ease-of-use, that is, the complexity of the model should be such as to allow anybody to use and apply the model. Second, the modeler must ensure that the values for the model parameters can be easily extracted 1 We include here the Markov-modulated Poisson processes that can model a self-similar process only when having an infinite number of states.

Although they can approach this goal by increasing the number of states, for tractability the number of states must remain in the range of two-four, which leads to the generation of non-self-similar traffic.

Definition 4. The quartiles are usually referred to as Qn , where Q1 is also called the lower quartile and is equal to the 25th percentile, and Q3 is also called the upper quartile and is equal to the 75th percentile. Let g X be P a real-valued function of the random variable X. Note that the mean of a distribution may not exist, but the median always exists. The central moments of a random variable X are its moments with respect to its mean.

P Definition 4. Thus, it is useful to model it such that the model reminds of the real process. Phase Type vs. Heavy-Tailed distributions Two classes of distributions occur often in the modeling of computer systems: the phase type distri- butions and the heavy-tailed distributions. Phase type distributions characterize well a system of one or more inter-related Poisson processes occurring in sequence phases. Examples of such distribu- tions that are commonly used are the exponential, the Erlang, the hyper-exponential, and the Coxian distributions.

The commonly used heavy-tailed distributions are the Lognormal, the Power, the Pareto, the Zipf, and Weibull. Commonly used distributions In the following we present the common distributions used in computer science. Table 4. For detailed descriptions regarding each of these distributions, and on derriving the mathematical formulas presented here, we refer to the textbook of Evans et al. The exponential distribution is used to model many computer-related processes, from the event inter-arrival time in a Poisson process to the service time required by jobs [Kle76].

The main advantage in using this distribution is that a system with independent jobs with exponential service time that arrive with exponential inter-arrival time can be modeled as a basic Markov chain, and the main performance characteristics of the system can be easily extracted.

The Erlang distribution is less variable than an exponential distribution with the same mean. The hyper-exponential distribution is a compound distribution comprising n exponential distribu- tions, each with its own rate. This can be seen as a system with several queues, where request arrival in each queue follows an exponential distribution.

The hyper-exponential distributions are more variable than an exponential distribution with the same mean. The Coxian distribution combines the properties of both Erlang and hyper-exponential distribu- tions into a distribution family that can exhibit both low and high variability. The gamma distribution is a good approximation for the time required to observe exactly k the shape parameter arrivals of Poisson events.

Gamma is a versatile distribution that can attain a wide variety of shapes and scales. Its variance can be much higher than for an exponential function of similar mean when the scale parameter is set accordingly. The normal distribution models well additive processes, that is, a system in which many statistically identical but independent users make requests.

The log-normal distribution models random variables with the logarithm normally distributed; it can be thought as the result of a multiplicative process over time, e. The log-normal distribution has higher variability than the normal distribution for the same mean. However, the power-law has also been wrongly attributed to any distribution with high right-skew and wide range of values; Newman [New05] argues that the log-normal distribution is often an alternative to empirical distributions believed to be Pareto.

The main advantage of a hyper-distribution is its mathematical tractability. The Hyper-Erlang distribution with two steps was used to model the request inter-arrival times in supercomputers [Jan97]. Other mixtures of distributions are possible, but their use is rare, as they raise the complexity of the mathematical analysis. This process involves three main steps: design, analysis, and modeling. Second, the important model components are selected from the set of all components.

The main focus in this sub-step is the ease-of-use of the model see Section 4. Third, the correlations between various characteristics are evaluated; the pair-wise correlations are the most commonly studied. The existence of a provable strong correlation necessarily leads to extending the model with aspects that describe the correlation. Design a Design model components.

Analysis a Determine the characteristics of the model components from real data. Modeling a Select candidate distributions based on analysis results. Figure 4.

The null-hypothesis is rejected if D is greater than the critical value obtained from the KS-test table. The KS-test is robust in outcome i. The KS-test can disprove the null-hypothesis, but cannot prove it.

However, a lower value of D indicates better similarity between the input data and data sampled from the theoretical distributions. The range of values for R2 is [0,1]; a low value of R2 e. Similarly to the KS-test, R2 cannot demonstrate the correlation between the model and the data. The use of R2 is limited in practice by its strong requirements: the number of model parameters needs to be small e.

The average of this set are taken as the parameters of the average system. We assume that in the short-term there are no changes in the performance of individual resources. Thus, for the remainder of this section we use the terms resource dynamics and resource availability interchangeably.

Local Resources – – – – – – – – – c1 c2 c3 c4 c5 c6 c7 c8 c9 c10 c11 c12 c13 c14 c15 64 64 92 Figure 4. Throughout this section we use the following terminology. We also investigate the notion of groups of unavailabilities, which we call correlated failures. Compared to traditional resource availability models [Tan93; Gra90; Zha04], ours adds the necessary link between the failures and the clusters where they occur.

Observed No. Each cluster comprises a set of dual-processor nodes; we use in this section the terms node and resource interchangeably. The number of processors per cluster is valid for 12 December, The traces record availability events with the following data: the node whose availability state changes i. Together, these traces comprise more than half a million individual availability events.

We follow the steps of the modeling process described in Section 4. For the analysis part, we consider three levels of data aggregation: the grid, the cluster, and the node level. The grid level aggregates un availability events for all the nodes in the grid. Similarly, the cluster level aggregates un availability events for all the nodes in the cluster.

The two curves are indistinguishable: failures are always followed by repairs. Analysis Results Figure 4. For both graphs, the left parts depict data for , and the right parts depict data for Thus, the ability of the grid to run grid-wide parallel jobs with a runtime above 20 minutes is questionable.

As expected, this value is much higher than the MTBF at the grid level. However, some nodes fail only once or even never during this period. Months after this, the now year-old Rancic departed E! Information and toy firm Mattel went on to launch a doll impressed by Zendaya with an identical purple carpet look and along with her gorgeous dreadlocks included. I hope that others negatively affected by her phrases may also discover it of their hearts to just accept her apology as nicely.

Martin Luther King, Jr. Along with new episodes of Euphoria coming our approach, Zendaya will probably be seen in Spider-Man: No Manner Dwelling which arrives in cinemas in December. Get in contact with us at webcelebs trinitymirror.

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