Autonomic Computing, International Conference on (2005)
June 13, 2005 to June 16, 2005
DOI Bookmark: http://doi.ieeecomputersociety.org/10.1109/ICAC.2005.63
Jonathan Wildstrom , University of Texas at Austin
Peter Stone , University of Texas at Austin
Emmett Witchel , University of Texas at Austin
Raymond J. Mooney , University of Texas at Austin
Mike Dahlin , University of Texas at Austin
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 the initial steps towards a system that can learn to alter its current configuration in reaction to the current workload. In particular, the advantages of shifting CPU and memory resources online are considered. Investigation on a publically available multi-machine, multi-process distributed system (the online transaction processing benchmark TPC-W) indicates that there is a real performance benefit to reconfiguration in reaction to workload changes. A learning framework is presented that does not require any instrumentation of the middleware, nor any special instrumentation of the operating system; rather, it learns to identify preferable configurations as well as their quantitative performance effects from system behavior as reported by standard monitoring tools. Initial results using the WEKA machine learning package suggest that automatic adaptive configuration can provide measurable performance benefits over any fixed configuration.
M. Dahlin, E. Witchel, J. Wildstrom, R. J. Mooney and P. Stone, "Towards Self-Configuring Hardware for Distributed Computer Systems," Autonomic Computing, International Conference on(ICAC), Seattle, Washington, 2005, pp. 241-249.