Wayne State University
At Wayne State University they are working on developing a unified learning approach, namely URL, to automate the configuration processes of virtualized machines and applications running on the virtual machines and adapt the systems configuration to the dynamics of cloud. This research is funded by a National Science Foundation (NSF) grant.
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A Unified Reinforcement Learning Approach for Autonomic Cloud Management |
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Cloud Computing, unlocked by virtualization, is emerging as an increasingly important service-oriented computing paradigm. The goal of this project is to develop a unified learning approach, namely URL, to automate the configuration processes of virtualized machines and applications running on the virtual machines and adapt the systems configuration to the dynamics of cloud.
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Paper: CoSL: A Coordinated Statistical Learning Approach to Measuring the Capacity of Multi-tier Websites  |
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October 22 2009
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By Jia Rao and Cheng-Zhong Xu.
Abstract: Website capacity determination is crucial to measurement-based access control, because it determines when to turn away excessive client requests to guarantee consistent service quality under overloaded conditions. Conventional capacity measurement approaches based on high-level performance metrics like response time and throughput may result in either resource over-provisioning or lack of responsiveness. It is because a website may have different capacities in terms of the maximum concurrent level when the characteristic of workload changes. Moreover, bottleneck in a multi-tier website may shift among tiers as client access pattern changes. In this paper, we present an online robust measurement approach based on statistical machine learning techniques. It uses a Bayesian network to correlate low level instrumentation data like system and user cpu time, available memory size, and I/O status that are collected at run-time to high level system states in each tier. A decision tree is induced over a group of coordinated Bayesian models in different tiers to identify the bottleneck dynamically when the system is overloaded. Experimental results demonstrate its accuracy and robustness in different traffic loads.
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