IBM Systems Journal - 2002 Copyright

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IBM Journal of Research and Development  
Volume 42, Number 1, Page 136 (2003)
Autonomic Computing
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Managing Web server performance with AutoTune agents

by Y. Diao, J. L. Hellerstein, S. Parekh, J. P. Bigus
Managing the performance of e-commerce sites is challenging. Site content changes frequently as do customer interests and business plans. All of these contribute to dynamically varying workloads. To maintain good performance, system administrators must tune their IT environment on an ongoing basis. Unfortunately, doing so requires considerable expertise and increases the total cost of system ownership. In this paper, we propose an agent-based solution which not only automates the ongoing system tuning but also automatically designs an appropriate tuning mechanism for the target system. We illustrate this in the context of managing a web server, where we study the problem of controlling CPU and memory utilization of an Apache® web server using the application-level tuning parameters MaxClients and KeepAlive which are exposed by the server. Using the AutoTune agent framework under the Agent Building and Learning Environment (ABLE), we construct agents to fully automate a control-theoretic methodology that involves model building, controller design, and run-time feedback control. Specifically, we design (i) a modeling agent that builds a dynamic system model from the controlled server run data, (ii) a controller design agent that uses optimal control theory to derive a feedback control algorithm customized to that server, and (iii) a run-time control agent that deploys the feedback control algorithm in an online real-time environment to automatically manage the web server. The designed autonomic feedback control system is able to handle the dynamic and interrelated dependencies between the tuning parameters and the performance metrics with guaranteed stability from control theory. The effectiveness of the AutoTune agents is demonstrated through experiments involving variations in workload, server capacity, and business objectives. The results also serve as a validation of the ABLE toolkit and the AutoTune agent framework.
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