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A Hierarchical Optimization Framework for Autonomic Performance Management of Distributed Computing Systems

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3 Author(s)
N. Kandasamy ; Drexel University, PA ; S. Abdelwahed ; M. Khandekar

This paper develops a scalable online optimization framework for the autonomic performance management of distributed computing systems operating in a dynamic environment to satisfy desired quality-ofservice objectives. To efficiently solve the performance management problems of interest in a distributed setting, we develop a hierarchical structure where a highlevel limited-lookahead controller manages interactions between lower-level controllers using forecast operating and environment parameters. We develop the overall control structure, and as a case study, show how to efficiently manage the power consumed by a computer cluster. Using workload traces from the Soccer World Cup 98 web site, we show via simulations that the proposed method is scalable, has low run-time overhead, and adapts quickly to time-varying workload patterns.

Published in:

26th IEEE International Conference on Distributed Computing Systems (ICDCS'06)

Date of Conference: