Research Article Open Access

Predictive Autonomicity of Web Services in the MAWeS Framework

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


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.

Journal of Computer Science
Volume 2 No. 6, 2006, 513-520


Submitted On: 8 February 2006 Published On: 30 June 2006

How to Cite: Mancini, E. P., Rak, M., Torella, R. & Villano, U. (2006). Predictive Autonomicity of Web Services in the MAWeS Framework. Journal of Computer Science, 2(6), 513-520.

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  • Autonomic
  • self-optimization
  • web services
  • performance prediction
  • simulation