Stochastic abstraction of programs : towards performance-driven development
نویسنده
چکیده
Distributed computer systems are becoming increasingly prevalent, thanks to moderntechnology, and this leads to significant challenges for the software developers of thesesystems. In particular, in order to provide a certain service level agreement with users,the performance characteristics of the system are critical. However, developers todaytypically consider performance only in the later stages of development, when it may betoo late to make major changes to the design. In this thesis, we propose a performance-driven approach to development — based around tool support that allows developersto use performance modelling techniques, while still working at the level of programcode.There are two central themes to the thesis. The first is to automatically relate per-formance models to program code. We define the Simple Imperative Remote Invoca-tion Language (S), and provide a probabilistic semantics that interprets a programas a Markov chain. To make such an interpretation both computable and efficient,we develop an abstract interpretation of the semantics, from which we can derive aPerformance Evaluation Process Algebra (PEPA) model of the system. This is basedaround abstracting the domain of variables to truncated multivariate normal measures.The second theme of the thesis is to analyse large performance models by meansof compositional abstraction. We use two abstraction techniques based on aggrega-tion of states — abstract Markov chains, and stochastic bounds — and apply both ofthem compositionally to PEPA models. This allows us to model check properties inthe three-valued Continuous Stochastic Logic (CSL), on abstracted models. We haveimplemented an extension to the Eclipse plug-in for PEPA, which provides a graphicalinterface for specifying which states in the model to aggregate, and for performing themodel checking.
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