High-end servers that can be partitioned into logical subsystems and repartitioned on the fly are now becoming available. This development raises the possibility of reconfiguring distributed systems online to optimize for dynamically changing workloads. This paper presents one approach to solving this online reconfiguration problem. In particular, we learn to identify, from only low-level system statistics, which of a set of possible configurations will lead to better performance under the current unknown workload. This approach requires no instrumentation of the system's middleware or operating systems. We introduce an agent that is able to learn this model and use it to switch configurations online as the workload varies. Our agent is fully implemented and tested on a publically available multi-machine, multi-process distributed system (the online transaction processing benchmark TPC-W). We demonstrate that our adaptive configuration is able to outperform any single fixed configuration in the set over a variety of workloads, including gradual changes and abrupt workload spikes.

PDF, HTML

Technical Report UT-AI-TR-06-330, The University of Texas at Austin, Department of Computer Sciences, AI Laboratory.

@techreport{Wildstrom06-tech, title={Adapting to Workload Changes Through On-The-Fly Reconfiguration}, author={Jonathan Wildstrom and Peter Stone and Emmett Witchel and Mike Dahlin}, number={UT-AI-TR-06-330}, month={June}, institution={The University of Texas at Austin, Department of Computer Sciences, AI Laboratory}, url="http://www.cs.utexas.edu/users/ai-lab?Wildstrom06-tech", year={2006} }