PREPARE: Predictive Performance Anomaly Prevention for Virtualized Cloud Systems
Virtualized cloud systems are prone to performance anomalies due to various reasons such as resource contentions, software bugs, and hardware failures. In this paper, we present a novel Predictive Performance Anomaly Prevention (PREPARE) system that provides automatic performance anomaly prevention...
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| creator | Yongmin Tan Hiep Nguyen Zhiming Shen Xiaohui Gu Venkatramani, C. Rajan, D. |
| description | Virtualized cloud systems are prone to performance anomalies due to various reasons such as resource contentions, software bugs, and hardware failures. In this paper, we present a novel Predictive Performance Anomaly Prevention (PREPARE) system that provides automatic performance anomaly prevention for virtualized cloud computing infrastructures. PREPARE integrates online anomaly prediction, learning-based cause inference, and predictive prevention actuation to minimize the performance anomaly penalty without human intervention. We have implemented PREPARE on top of the Xen platform and tested it on the NCSU's Virtual Computing Lab using a commercial data stream processing system (IBM System S) and an online auction benchmark (RUBiS). The experimental results show that PREPARE can effectively prevent performance anomalies while imposing low overhead to the cloud infrastructure. |
| doi_str_mv | 10.1109/ICDCS.2012.65 |
| format | Conference Proceeding |
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| ispartof | 2012 IEEE 32nd International Conference on Distributed Computing Systems, 2012, p.285-294 |
| issn | 1063-6927 2575-8411 |
| language | eng |
| recordid | cdi_ieee_primary_6258001 |
| source | IEEE Electronic Library (IEL) Conference Proceedings |
| subjects | Bayesian methods Benchmark testing Cloud computing Markov processes Measurement Monitoring online anomaly prediction performance anomaly prevention Predictive models |
| title | PREPARE: Predictive Performance Anomaly Prevention for Virtualized Cloud Systems |
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