Platform-independent modeling and prediction of application resource usage characteristics
Application resource usage models can be used in the decision making process for ensuring quality-of-service as well as for capacity planning, apart from their general use in performance modeling, optimization, and systems management. Current solutions for modeling application resource usage tend to...
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| Published in | The Journal of systems and software Vol. 82; no. 12; pp. 2117 - 2127 |
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| Main Authors | , , , |
| Format | Journal Article |
| Language | English |
| Published |
New York
Elsevier Inc
01.12.2009
Elsevier Sequoia S.A |
| Subjects | |
| Online Access | Get full text |
| ISSN | 0164-1212 1873-1228 |
| DOI | 10.1016/j.jss.2009.07.020 |
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| Abstract | Application resource usage models can be used in the decision making process for ensuring quality-of-service as well as for capacity planning, apart from their general use in performance modeling, optimization, and systems management. Current solutions for modeling application resource usage tend to address parts of the problem by either focusing on a specific application, or a specific platform, or on a small subset of system resources. We propose a simple and flexible approach for
modeling application resource usage in a platform-independent manner that enables the prediction of application resource usage on
unseen platforms. The technique proposed is application agnostic, requiring no modification to the application (binary or source) and no knowledge of application-semantics. We implement a Linux-based prototype and evaluate it using four different workloads including real-world applications and benchmarks. Our experiments reveal prediction errors that are bound within 6–24% of the observed for these workloads when using the proposed approach. |
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| AbstractList | Application resource usage models can be used in the decision making process for ensuring quality-of-service as well as for capacity planning, apart from their general use in performance modeling, optimization, and systems management. Current solutions for modeling application resource usage tend to address parts of the problem by either focusing on a specific application, or a specific platform, or on a small subset of system resources. We propose a simple and flexible approach for modeling application resource usage in a platform-independent manner that enables the prediction of application resource usage on unseen platforms. The technique proposed is application agnostic, requiring no modification to the application (binary or source) and no knowledge of application-semantics. We implement a Linux-based prototype and evaluate it using four different workloads including real-world applications and benchmarks. Our experiments reveal prediction errors that are bound within 6-24% of the observed for these workloads when using the proposed approach. Application resource usage models can be used in the decision making process for ensuring quality-of-service as well as for capacity planning, apart from their general use in performance modeling, optimization, and systems management. Current solutions for modeling application resource usage tend to address parts of the problem by either focusing on a specific application, or a specific platform, or on a small subset of system resources. We propose a simple and flexible approach for modeling application resource usage in a platform-independent manner that enables the prediction of application resource usage on unseen platforms. The technique proposed is application agnostic, requiring no modification to the application (binary or source) and no knowledge of application-semantics. We implement a Linux-based prototype and evaluate it using four different workloads including real-world applications and benchmarks. Our experiments reveal prediction errors that are bound within 6-24% of the observed for these workloads when using the proposed approach. [PUBLICATION ABSTRACT] Application resource usage models can be used in the decision making process for ensuring quality-of-service as well as for capacity planning, apart from their general use in performance modeling, optimization, and systems management. Current solutions for modeling application resource usage tend to address parts of the problem by either focusing on a specific application, or a specific platform, or on a small subset of system resources. We propose a simple and flexible approach for modeling application resource usage in a platform-independent manner that enables the prediction of application resource usage on unseen platforms. The technique proposed is application agnostic, requiring no modification to the application (binary or source) and no knowledge of application-semantics. We implement a Linux-based prototype and evaluate it using four different workloads including real-world applications and benchmarks. Our experiments reveal prediction errors that are bound within 6–24% of the observed for these workloads when using the proposed approach. |
| Author | Duran-Limon, Hector A. Corona-Perez, Manuel Rangaswami, Raju Shimizu, Shuichi |
| Author_xml | – sequence: 1 givenname: Shuichi surname: Shimizu fullname: Shimizu, Shuichi email: shue@jp.ibm.com organization: IBM Tokyo Research Laboratory, Kanagawa, Japan – sequence: 2 givenname: Raju surname: Rangaswami fullname: Rangaswami, Raju email: raju@cs.fiu.edu organization: Florida International University, Miami, FL, USA – sequence: 3 givenname: Hector A. surname: Duran-Limon fullname: Duran-Limon, Hector A. email: hduran@cucea.udg.mx organization: University of Guadalajara, 45100 CUCEA, Mexico – sequence: 4 givenname: Manuel surname: Corona-Perez fullname: Corona-Perez, Manuel email: manuel.corona@red.cucei.udg.mx organization: University of Guadalajara, 45100 CUCEA, Mexico |
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| Cites_doi | 10.1109/CLUSTR.2005.347062 10.1109/32.58769 10.1109/IPDPS.2002.1015480 10.1023/A:1015634802585 10.1007/BFb0053984 10.1023/A:1019052825453 10.1145/1341811.1341854 10.2307/2683825 10.1007/978-0-387-78446-5_10 10.1109/IPDPS.2008.4536214 10.1016/j.future.2006.02.008 10.1023/A:1019048724544 10.1117/12.706009 10.1109/ICGRID.2006.311027 10.1109/HPCA.2007.346211 10.1007/978-3-540-24774-6_16 10.1145/1012888.1005691 10.1007/978-3-540-24689-3_32 |
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| Keywords | Resource usage prediction Resource model Platform-independent resource model QoS management |
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| References_xml | – reference: Katcher, 1997. PostMark: A New File System Benchmark, < – reference: Snedecor, G.W., Cochran, W.G., 1980. Statistical Methods, seventh ed. The Iowa State University Press. – start-page: 40 year: 2005 end-page: 49 ident: bib34 article-title: Cross-platform performance prediction of parallel applications using partial execution publication-title: Proceedings of Supercomputing – reference: Lee, B.C., Brooks, D.M., 2007. Illustrative design space studies with microarchitectural regression models. In: Proceedings of the IEEE International Symposium on High Performance Computer Architecture. – reference: Lee, B.C., Brooks, D.M., 2006. Efficiently exploring architectural design spaces via predictive modeling. 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| SubjectTerms | Benchmarks Computer programs Decision making models Focusing Mathematical models Optimization Platform-independent resource model Platforms QoS management Quality of service Resource model Resource usage prediction Semantics Studies Systems management Workload Workloads |
| Title | Platform-independent modeling and prediction of application resource usage characteristics |
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