Journal of Computer Science

Predictive Autonomicity of Web Services in the MAWeS Framework

Emilio P. Mancini, Massimiliano Rak, Roberto Torella and Umberto Villano

DOI : 10.3844/jcssp.2006.513.520

Journal of Computer Science

Volume 2, Issue 6

Pages 513-520


In Web Services designs classical optimization techniques are not applicable. A possible solution to guarantee critical requirements is the use of an autonomic architecture, able to auto-configure and to auto-tune. This study presents MAWeS (MetaPL/HeSSE Autonomic Web Services), a framework whose aim is to support the development of self-optimizing predictive autonomic systems for Web service architectures. It adopts a simulation-based methodology, which allows to predict system performance in different status and load conditions. The predicted results are used for a feedforward control of the system, which self-tunes before the new conditions and the subsequent performance losses are actually observed.


© 2006 Emilio P. Mancini, Massimiliano Rak, Roberto Torella and Umberto Villano. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.