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

Authors:
Nagarajan Kandasamy;Sherif Abdelwahed;Mohit Khandekar
Affiliations:
Drexel University, PA;Vanderbilt University, TN;Drexel University, PA
Venue:
ICDCS '06 Proceedings of the 26th IEEE International Conference on Distributed Computing Systems
Year:
2006

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Abstract

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.