Abstract
A possible solution to guarantee critical requirements in Web services designs is the use of an autonomic architecture, able to auto-configure and to auto-tune. This paper presents an innovative approach for the development of self-optimizing autonomic systems for Web services architectures, based on the adoption of a simulation engine for obtaining performance predictions. MAWeS (MetaPL/HeSSE Autonomic Web Services) is 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 performances 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
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