Performance evaluation with asynchronously decomposable SWN: implementation and case study

Delamare, C.; Gardan, Yvon; Moreaux, Patrice

doi:10.1109/pnpm.2003.1231539

Cited by 9 publications

(14 citation statements)

References 9 publications

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“…Moreover, they combine lumping techniques with symbolic techniques resulting in a new algorithm that symbolically generates the state space of a hierarchical model built using join and replicate operators in the form of an MDD and then obtains an MDD representation of the lumped state space starting from the MDD generated by the state-space generation algorithm. Other researchers have devised approaches to combine lumping due to structural symmetries with compact representation using a Kronecker representation (Benoit, Brenner, Fernandes, and Plateau 2004) and symbolic data structures (Delamare, Gardan, and Moreaux 2003).…”

Section: Also Inmentioning

confidence: 99%

Two-layer symbolic representation for stochastic models with phase-type distributed events

Longo

Scarpa

2013

International Journal of Systems Science

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Among the techniques that have been proposed for the analysis of non-Markovian models, the state space expansion approach showed great flexibility in terms of modelling capacities.The principal drawback is the explosion of the state space. This paper proposes a two-layer symbolic method for efficiently storing the expanded reachability graph of a non-Markovian model in the case in which continuous phase-type distributions are associated with the firing times of system events, and different memory policies are considered. At the lower layer, the reachability graph is symbolically represented in the form of a set of Kronecker matrices, while, at the higher layer, all the information needed to correctly manage event memory is stored in a multi-terminal multi-valued decision diagram. Such an information is collected by applying a symbolic algorithm, which is based on a couple of theorems. The efficiency of the proposed approach, in terms of memory occupation and execution time, is shown by applying it to a set of non-Markovian stochastic Petri nets and comparing it with a classical explicit expansion algorithm. Moreover, a comparison with a classical symbolic approach is performed whenever possible.

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Section: Also Inmentioning

confidence: 99%

Two-layer symbolic representation for stochastic models with phase-type distributed events

Longo

Scarpa

2013

International Journal of Systems Science

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“…For this purpose, we devise an extension of our previous work [8,9] adapted to CBS. Let us remind that this approach defines a structured analysis method for a decomposition of SWNs, allowing to avoid explicit construction of the aggregated Markov chain corresponding to the global SWN.…”

Section: Structured Performance Analysis Of Cbsmentioning

confidence: 99%

“…We model these interactions and we show how to build the global SWN of the CBS. Finally, we apply a structured method, derived from our previous works [8,9], allowing us to compute performance indices in an efficient way, through exploitation of the compositional architecture. Computations are based on a combined aggregation/tensorial representation of the underlying Markov chain of the global SWN, which reduces the complexity of analysis, with time and memory savings.…”

Section: Introductionmentioning

confidence: 99%

Structured performance analysis for component-based systems

Salmi

Moreaux

Ioualalen

2012

IJCCBS

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Abstract:The Component Based System (CBS) paradigm is now largely used to design software systems. In addition, performance and behavioural analysis remains a required step for the design and the construction of efficient systems. This is especially the case of CBS, which involve interconnected components running concurrent processes. This paper proposes a compositional method for modeling and structured performance analysis of CBS. Modeling is based on Stochastic Well-formed Nets (SWN), a high level model of Stochastic Petri nets, widely used for dependability analysis of concurrent systems. Starting from the definition of the system given in a suitable Architecture Description Language, and from the definition of the elementary components, we build an SWN of the global system together with a set of SWNs modeling the components of the CBS and their connections. From these models, we derive performances of the system thanks to a structured analysis induced by the structure of the CBS. We describe the application of our method through an example designed in the framework of the CORBA Component Model.

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“…In our approach, we rather try to benefit from the compositionality features of the CBS in order to provide an efficient steady-state performance analysis with regard to computation time and memory costs. For this purpose, we rely on previous work [12,13], which defines an analysis method allowing to avoid the explicit construction of the aggregated Markov chain corresponding to the global SWN. This method uses a tensorial representation of the Symbolic Reachability Graph(SRG), hence enabling important memory and computation time savings, reducing analysis complexity.…”

Section: Performance Analysis Of Fractal Cbssmentioning

confidence: 99%

“…Finally, we apply, when possible, a structured method allowing to compute performance indices. In order to apply this structured compositional method, we derive from the architecture of the CBS, a compositional view of the system at the SWN level, and we apply a modified version of our previous works [12,13] which proposed a structured analysis method for either a synchronous or asynchronous decomposition of SWNs. This method is based on a combined aggregation/tensorial representation of the underlying Markov chain of the global SWN.…”

Section: Introductionmentioning

confidence: 99%

Performance evaluation of Fractal component-based systems

Salmi¹,

Moreaux²,

Ioualalen³

2008

Ann. Telecommun.

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Component-based system development is now a well accepted design approach in software engineering. Numerous component models have been proposed, and for most of them, specific software tools allow building component-based systems (CBS). Although these tools perform several checks on the built system, few of them provide formal verification of behavioural properties nor performance evaluation of the resulting system. In this context, we have developed a general method associating to a CBS, a formal model, based on stochastic well formed nets, a class of high-level Petri nets, allowing qualitative behavioural analysis together with performance evaluation of this CBS. The definition of the model heavily depends on the (run time) component model used to describe the CBS. In this paper, we instantiate our method to Fractal CBS and its reference Java implementation Julia. The method starts from the Fractal architectural description of a system and defines rules to systematically generate element models of the CBS and their interactions. We then apply a structured method for both qualitative and performance analysis, taking into account the given implementation of the Fractal model. The main interest of our method is to take advantage of the compositional definition of such systems to carry out an efficient analysis. The paper concentrates on performance evaluation and presents our method step by step with an illustrative example.

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Performance evaluation with asynchronously decomposable SWN: implementation and case study

Cited by 9 publications

References 9 publications

Two-layer symbolic representation for stochastic models with phase-type distributed events

Two-layer symbolic representation for stochastic models with phase-type distributed events

Structured performance analysis for component-based systems

Performance evaluation of Fractal component-based systems

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