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 autoconfigure 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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SUMMARYThis paper describes a simulation-based technique for the performance prediction of message-passing applications on cluster systems by means of benchmark data. Given data measuring the performance of a target cluster in the form of standard benchmark results, along with the details of the chosen computing configuration, it is possible to build and to validate automatically a detailed simulation model. This makes it possible to predict off-line, i.e. without resorting to the real hardware, the performance of fully developed or even of skeletal code. An XML-based language (MetaPL) is adopted to describe the application behavior in the development stage. After a description of the approach and the illustration of the construction and validation of the simulation model, the paper presents a case study.

Abstract. Historically, high performance systems use schedulers and intelligent resource managers in order to optimize system usage and application performance. Most of the times, applications just issue requests of resources to the central system. This centralized approach is an unnecessary constraint for a class of potentially flexible applications, whose resource usage may be modulated as a function of the system status. In this paper we propose a tool which, in a way essentially transparent to final users, lets the application to self-tune in function of the status of the target execution environment. The approach hinges on the use of the MetaPL/HeSSE methodology, i.e., on the use of simulation to predict execution times and skeletal descriptions of the application to describe run-time resource usage.