White-Box Performance-Influence Models: A Profiling and Learning Approach
Many modern software systems are highly configurable, allowing the user to tune them for performance and more. Current performance modeling approaches aim at finding performance-optimal configurations by building performance models in a black-box manner. While these models provide accurate estimates...
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| Vydáno v: | Proceedings / International Conference on Software Engineering s. 1059 - 1071 |
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| Hlavní autoři: | , , |
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| Jazyk: | angličtina |
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IEEE
01.05.2021
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| ISBN: | 1665402962, 9781665402965 |
| ISSN: | 1558-1225 |
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| Abstract | Many modern software systems are highly configurable, allowing the user to tune them for performance and more. Current performance modeling approaches aim at finding performance-optimal configurations by building performance models in a black-box manner. While these models provide accurate estimates, they cannot pinpoint causes of observed performance behavior to specific code regions. This does not only hinder system understanding, but it also complicates tracing the influence of configuration options to individual methods. We propose a white-box approach that models configuration-dependent performance behavior at the method level. This allows us to predict the influence of configuration decisions on individual methods, supporting system understanding and performance debugging. The approach consists of two steps: First, we use a coarse-grained profiler and learn performance-influence models for all methods, potentially identifying some methods that are highly configuration-and performance-sensitive, causing inaccurate predictions. Second, we re-measure these methods with a fine-grained profiler and learn more accurate models, at higher cost, though. By means of 9 real-world Java software systems, we demonstrate that our approach can efficiently identify configuration-relevant methods and learn accurate performance-influence models. |
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| AbstractList | Many modern software systems are highly configurable, allowing the user to tune them for performance and more. Current performance modeling approaches aim at finding performance-optimal configurations by building performance models in a black-box manner. While these models provide accurate estimates, they cannot pinpoint causes of observed performance behavior to specific code regions. This does not only hinder system understanding, but it also complicates tracing the influence of configuration options to individual methods. We propose a white-box approach that models configuration-dependent performance behavior at the method level. This allows us to predict the influence of configuration decisions on individual methods, supporting system understanding and performance debugging. The approach consists of two steps: First, we use a coarse-grained profiler and learn performance-influence models for all methods, potentially identifying some methods that are highly configuration-and performance-sensitive, causing inaccurate predictions. Second, we re-measure these methods with a fine-grained profiler and learn more accurate models, at higher cost, though. By means of 9 real-world Java software systems, we demonstrate that our approach can efficiently identify configuration-relevant methods and learn accurate performance-influence models. |
| Author | Siegmund, Norbert Weber, Max Apel, Sven |
| Author_xml | – sequence: 1 givenname: Max surname: Weber fullname: Weber, Max organization: Leipzig University, Germany – sequence: 2 givenname: Sven surname: Apel fullname: Apel, Sven organization: Saarland University, Saarland Informatics Campus, Germany – sequence: 3 givenname: Norbert surname: Siegmund fullname: Siegmund, Norbert organization: Leipzig University, Germany |
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| Snippet | Many modern software systems are highly configurable, allowing the user to tune them for performance and more. Current performance modeling approaches aim at... |
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| SubjectTerms | configuration management Debugging Java Predictive models Software engineering Software measurement software product lines Software systems software variability Testing |
| Title | White-Box Performance-Influence Models: A Profiling and Learning Approach |
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