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

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.

• Type: Conference
• Year: 2006
• Where: ICDCS
• Authors: Nagarajan Kandasamy, Sherif Abdelwahed, Mohit Khandekar
• Keywords: Distributed And Parallel Computing, ICDCS 2006, Performance Management, Scalable Online Optimization, Time-varying Workload Patterns