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| Content Provider | IEEE Xplore Digital Library |
|---|---|
| Author | Jia Rao Cheng-Zhong Xu |
| Copyright Year | 2008 |
| Description | Author affiliation: Dept. of Electr. & Comput. Eng., Wayne State Univ., Detroit, MI (Jia Rao; Cheng-Zhong Xu) |
| Abstract | Understanding server capacity is crucial for system capacity planning, configuration, and QoS-aware resource management. Conventional stress testing approaches measure the server capacity in terms of application-level performance metrics like response time and throughput. They are limited in measurement accuracy and timeliness. In a multitier website, resource bottleneck often shifts between tiers as client access pattern changes. This makes the capacity measurement even more challenging. This paper presents a measurement approach based on hardware performance counter metrics. The approach uses machine learning techniques to infer application-level performance at each tier. A coordinated predictor is induced over individual tier models to estimate system-wide performance and identify the bottleneck when the system becomes overloaded. Experimental results demonstrate that this approach is able to achieve an overload prediction accuracy of higher than 90% for a priori known input traffic patterns and over 85% accuracy even for traffic causing frequent bottleneck shifting. It costs less than 0.5% runtime overhead for data collection and no more than 50 ms for each on-line decision. |
| Starting Page | 705 |
| Ending Page | 712 |
| File Size | 309445 |
| Page Count | 8 |
| File Format | |
| ISBN | 9780769531724 |
| ISSN | 10636927 |
| DOI | 10.1109/ICDCS.2008.97 |
| Language | English |
| Publisher | Institute of Electrical and Electronics Engineers, Inc. (IEEE) |
| Publisher Date | 2008-06-17 |
| Publisher Place | China |
| Access Restriction | Subscribed |
| Rights Holder | Institute of Electrical and Electronics Engineers, Inc. (IEEE) |
| Subject Keyword | Servers Hardware Training Accuracy Radiation detectors Runtime Time factors |
| Content Type | Text |
| Resource Type | Article |
| Subject | Computer Networks and Communications Hardware and Architecture Software |
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