VCONF: a reinforcement learning approach to virtual machines auto-configuration
ICAC '09 Proceedings of the 6th international conference on Autonomic computing
Journal of Network and Computer Applications
DynaQoS: model-free self-tuning fuzzy control of virtualized resources for QoS provisioning
Proceedings of the Nineteenth International Workshop on Quality of Service
URL: A unified reinforcement learning approach for autonomic cloud management
Journal of Parallel and Distributed Computing
Regression-based resource provisioning for session slowdown guarantee in multi-tier Internet servers
Journal of Parallel and Distributed Computing
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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.