Predictive Autonomicity of Web Services in the MAWeS Framework | Zendy

Emilio P. Mancini | Zendy; Massimiliano Rak | Zendy; Roberto Torella | Zendy; Umberto Villano | Zendy

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Predictive Autonomicity of Web Services in the MAWeS Framework

Author(s) -

Emilio P. Mancini,

Massimiliano Rak,

Roberto Torella,

Umberto Villano

Publication year - 2006

Publication title -

journal of computer science

Language(s) - English

Resource type - Journals

SCImago Journal Rank - 0.161

H-Index - 28

eISSN - 1552-6607

pISSN - 1549-3636

DOI - 10.3844/jcssp.2006.513.520

Subject(s) - computer science , world wide web

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.

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