Abstract
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.
Highlights
The use of Web Services architectures is becoming a customary approach for the development of open, large-scale interoperable systems[1,2,3,4,5], and there are many examples of “working” solutions
In this study we describe MAWeS (MetaPL/HeSSE Autonomic Web Services), a framework whose aim is to support the development of self-optimizing predictive autonomic systems for Web Services architectures
Of the load prediction method adopted and of optimization criteria chosen, the unit always works as follows: * it obtains from the MAWeS Interface the application MetaPL description and the list of parameters to be optimized; * it builds the application MetaPL description and the simulator configuration files; * it builds a set of metafilter scripts; implementation, the only possible optimization target * it starts as many M/H clients as the number of is response time minimization, and the only different simulations required; target of optimization is a minimization rule
Summary
The use of Web Services architectures is becoming a customary approach for the development of open, large-scale interoperable systems[1,2,3,4,5], and there are many examples of “working” solutions. In this study we tackle a new problem, i.e., how to automatize the simulation and performance prediction process, in order to develop self-optimization autonomic systems for Web Services architectures.
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