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Qualitative Performance Analysis for Large-Scale Scientific Workflows

dc.contributor.advisor Reed, Daniel A Buneci, Emma 2008-08-15T11:56:57Z 2008-08-15T11:56:57Z 2008-05-30
dc.description.abstract <p>Today, large-scale scientific applications are both data driven and distributed. To support the scale and inherent distribution of these applications, significant heterogeneous and geographically distributed resources are required over long periods of time to ensure adequate performance. Furthermore, the behavior of these applications depends on a large number of factors related to the application, the system software, the underlying hardware, and other running applications, as well as potential interactions among these factors.</p> <p>Most Grid application users are primarily concerned with obtaining the result of the application as fast as possible, without worrying about the details involved in monitoring and understanding factors affecting application performance. In this work, we aim to provide the application users with a simple and intuitive performance evaluation mechanism during the execution time of their long-running Grid applications or workflows. Our performance evaluation mechanism provides a qualitative and periodic assessment of the application's behavior by informing the user whether the application's performance is expected or unexpected. Furthermore, it can help improve overall application performance by informing and guiding fault-tolerance services when the application exhibits persistent unexpected performance behaviors.</p> <p>This thesis addresses the hypotheses that in order to qualitatively assess application behavioral states in long-running scientific Grid applications: (1) it is necessary to extract temporal information in performance time series data, and that (2) it is sufficient to extract variance and pattern as specific examples of temporal information. Evidence supporting these hypotheses can lead to the ability to qualitatively assess the overall behavior of the application and, if needed, to offer a most likely diagnostic of the underlying problem.</p> <p>To test the stated hypotheses, we develop and evaluate a general <em> qualitative performance analysis</em> framework that incorporates (a) techniques from time series analysis and machine learning to extract and learn from data, structural and temporal features associated with application performance in order to reach a qualitative interpretation of the application's behavior, and (b) mechanisms and policies to reason over time and across the distributed resource space about the behavior of the application. </p> <p>Experiments with two scientific applications from meteorology and astronomy comparing signatures generated from instantaneous values of performance data versus those generated from temporal characteristics support the former hypothesis that temporal information is necessary to extract from performance time series data to be able to accurately interpret the behavior of these applications. Furthermore, temporal signatures incorporating variance and pattern information generated for these applications reveal signatures that have distinct characteristics during well-performing versus poor-performing executions. This leads to the framework's accurate classification of instances of similar behaviors, which represents supporting evidence for the latter hypothesis. The proposed framework's ability to generate a qualitative assessment of performance behavior for scientific applications using temporal information present in performance time series data represents a step towards simplifying and improving the quality of service for Grid applications.</p>
dc.format.extent 4788576 bytes
dc.format.mimetype application/pdf
dc.language.iso en_US
dc.subject Computer Science
dc.subject Computer Science
dc.subject feature selection
dc.subject time series analysis
dc.subject performance analysis
dc.subject signatures
dc.subject scientific applications
dc.title Qualitative Performance Analysis for Large-Scale Scientific Workflows
dc.type Dissertation
dc.department Computer Science

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