The PEPA workbench: a tool to support a process algebra-based approach to performance modelling
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A compositional approach to performance modelling
A compositional approach to performance modelling
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IEEE Transactions on Dependable and Secure Computing
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Journal of Computer and System Sciences
Partial Evaluation of PEPA Models for Fluid-Flow Analysis
EPEW '08 Proceedings of the 5th European Performance Engineering Workshop on Computer Performance Engineering
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Formal Methods for Web Services
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Electronic Notes in Theoretical Computer Science (ENTCS)
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SFM'07 Proceedings of the 7th international conference on Formal methods for performance evaluation
A fluid analysis framework for a Markovian process algebra
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IPDPS'06 Proceedings of the 20th international conference on Parallel and distributed processing
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FMCO'09 Proceedings of the 8th international conference on Formal methods for components and objects
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ASMTA'12 Proceedings of the 19th international conference on Analytical and Stochastic Modeling Techniques and Applications
Compositional approximate markov chain aggregation for PEPA models
EPEW'12 Proceedings of the 9th European conference on Computer Performance Engineering
Compositional approximate markov chain aggregation for PEPA models
EPEW'12 Proceedings of the 9th European conference on Computer Performance Engineering
Tackling continuous state-space explosion in a Markovian process algebra
Theoretical Computer Science
Efficient optimization of software performance models via parameter-space pruning
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Performance Evaluation Process Algebra (PEPA) is a formal language for performance modeling based on process algebra. It has previously been shown that, by using the process algebra apparatus, compact performance models can be derived which retain the essential behavioral characteristics of the modeled system. However, no efficient algorithm for this derivation was given. In this paper, we present an efficient algorithm which recognizes and takes advantage of symmetries within the model and avoids unnecessary computation. The algorithm is illustrated by a multiprocessor example.