A Hierarchical Optimization Framework for Autonomic Performance Management of Distributed Computing Systems
July 4, 2006 to July 7, 2006
Nagarajan Kandasamy , Drexel University, PA
Sherif Abdelwahed , Vanderbilt University, TN
Mohit Khandekar , Drexel University, PA
DOI Bookmark: http://doi.ieeecomputersociety.org/10.1109/ICDCS.2006.8
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.
Nagarajan Kandasamy, Sherif Abdelwahed, Mohit Khandekar, "A Hierarchical Optimization Framework for Autonomic Performance Management of Distributed Computing Systems", ICDCS, 2006, 26th IEEE International Conference on Distributed Computing Systems, 26th IEEE International Conference on Distributed Computing Systems 2006, pp. 9, doi:10.1109/ICDCS.2006.8