An efficient algorithm for aggregating PEPA models

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. 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

Extended Differential Aggregations in Process Algebra for Performance and Biology

We study aggregations for ordinary differential equations induced by fluid semantics for Markovian process algebra which can capture the dynamics of performance models and chemical reaction networks. Whilst previous work has required perfect symmetry for exact aggregation, we present approximate fluid lumpability, which makes nearby processes perfectly symmetric after a perturbation of their parameters. We prove that small perturbations yield nearby differential trajectories. Numerically, we show that many heterogeneous processes can be aggregated with negligible errors.Comment: In Proceedings QAPL 2014, arXiv:1406.156

Spatial extension of stochastic Pi calculus

We introduce a spatial extension of stochastic pi-calculus that provides a formalism to model systems of discrete, connected locations. We define the extended stochastic semantics and also give deterministic semantics in terms of a system of ordinary differential equations. We describe two simple examples, one based on a standard epidemic model and one modelling resistance in plant tissues

Proportional lumpability and proportional bisimilarity

3noIn this paper, we deal with the lumpability approach to cope with the state space explosion problem inherent to the computation of the stationary performance indices of large stochastic models. The lumpability method is based on a state aggregation technique and applies to Markov chains exhibiting some structural regularity. Moreover, it allows one to efficiently compute the exact values of the stationary performance indices when the model is actually lumpable. The notion of quasi-lumpability is based on the idea that a Markov chain can be altered by relatively small perturbations of the transition rates in such a way that the new resulting Markov chain is lumpable. In this case, only upper and lower bounds on the performance indices can be derived. Here, we introduce a novel notion of quasi-lumpability, named proportional lumpability, which extends the original definition of lumpability but, differently from the general definition of quasi-lumpability, it allows one to derive exact stationary performance indices for the original process. We then introduce the notion of proportional bisimilarity for the terms of the performance process algebra PEPA. Proportional bisimilarity induces a proportional lumpability on the underlying continuous-time Markov chains. Finally, we prove some compositionality results and show the applicability of our theory through examples.openopenMarin A.; Piazza C.; Rossi S.Marin, A.; Piazza, C.; Rossi, S

Practical applications of performance modelling of security protocols using PEPA

PhD ThesisTrade-off between security and performance has become an intriguing area in recent years in both the security and performance communities. As the security aspects of security protocol research is fully- edged, this thesis is therefore devoted to conducting a performance study of these protocols. The long term objective is to translate formal de nitions of security protocols to formal performance models automatically, then analysing by relevant techniques. In this thesis, we take a preliminary step by studying five typical security protocols, and exploring the methodology of construction and analysis of their models by using the Markovian process algebra PEPA. Through these case studies, an initial framework of performance analysis of security protocol is established. Firstly, a key distribution centre is investigated. The basic model su ers from the commonly encountered state space explosion problem, and so we apply some efficient solution techniques, which include model reduction techniques and ordinary di fferential equation based fluid flow analysis. Finally, we evaluate a utility function for this secure key exchange model. Then, we explore two non-repudiation protocols. Mean value analysis has been applied here for a class of PEPA models, and it is compared with an ODE approximation. After that, an optimistic nonrepudiation protocol with off-line third trust party is studied. The PEPA model has been formulated using a concept of multi-threaded servers with functional rates. The nal case study is a cross-realm Kerberos protocol. A simplified technique of aggregation with an ODE approximation is performed to do efficient cient analysis. All these modelling and analysis methods are illustrated through numerical examples

Evaluating the Robustness of Resource Allocations Obtained through Performance Modeling with Stochastic Process Algebra

Recent developments in the field of parallel and distributed computing has led to a proliferation of solving large and computationally intensive mathematical, science, or engineering problems, that consist of several parallelizable parts and several non-parallelizable (sequential) parts. In a parallel and distributed computing environment, the performance goal is to optimize the execution of parallelizable parts of an application on concurrent processors. This requires efficient application scheduling and resource allocation for mapping applications to a set of suitable parallel processors such that the overall performance goal is achieved. However, such computational environments are often prone to unpredictable variations in application (problem and algorithm) and system characteristics. Therefore, a robustness study is required to guarantee a desired level of performance. Given an initial workload, a mapping of applications to resources is considered to be robust if that mapping optimizes execution performance and guarantees a desired level of performance in the presence of unpredictable perturbations at runtime. In this research, a stochastic process algebra, Performance Evaluation Process Algebra (PEPA), is used for obtaining resource allocations via a numerical analysis of performance modeling of the parallel execution of applications on parallel computing resources. The PEPA performance model is translated into an underlying mathematical Markov chain model for obtaining performance measures. Further, a robustness analysis of the allocation techniques is performed for finding a robustmapping from a set of initial mapping schemes. The numerical analysis of the performance models have confirmed similarity with the simulation results of earlier research available in existing literature. When compared to direct experiments and simulations, numerical models and the corresponding analyses are easier to reproduce, do not incur any setup or installation costs, do not impose any prerequisites for learning a simulation framework, and are not limited by the complexity of the underlying infrastructure or simulation libraries