Stochastic Model Checking of the Stochastic Quality Calculus

Nielson, Flemming; Nielson, Hanne Riis; Zeng, Kebin

doi:10.1007/978-3-319-15545-6_30

Cited by 3 publications

(4 citation statements)

References 16 publications

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“…However, the stochastic behaviour (Exp distributed) and the real time behaviour (Dirac distributed) can often be split into a pure stochastic behaviour (Exp distributed) in the form of a Continuous Time Markov Chain [23] and a pure real time behaviour (Dirac distributed) embodied in a query in Continuous Stochastic Logic [24]. These ideas are developed in [26].…”

Section: Theorem 3 the Semantics Of Systems In The Stochastic Qualitmentioning

confidence: 99%

The Stochastic Quality Calculus

Zeng¹,

Nielson²,

Nielson³

2014

Lecture Notes in Computer Science

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International audienceWe introduce the Stochastic Quality Calculus in order to model and reason about distributed processes that rely on each other in order to achieve their overall behaviour. The calculus supports broadcast communication in a truly concurrent setting. Generally distributed delays are associated with the outputs and at the same time the inputs impose constraints on the waiting times. Consequently, the expected inputs may not be available when needed and therefore the calculus allows to express the absence of data.The communication delays are expressed by general distributions and the resulting semantics is given in terms of Generalised Semi-Markov Decision Processes. By restricting the distributions to be continuous and by allowing truly concurrent communication we eliminate the non-determinism and arrive at Generalised Semi-Markov Processes (GSMPs); further restriction to exponential distributions gives rise to numerically analysable GSMPs, in particular using techniques from stochastic model checking

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Section: Theorem 3 the Semantics Of Systems In The Stochastic Qualitmentioning

confidence: 99%

The Stochastic Quality Calculus

Zeng¹,

Nielson²,

Nielson³

2014

Lecture Notes in Computer Science

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“…The case in point here is whether a service endpoint providing a service can continue to maintain the robustness of that service if larger and larger numbers of attackers threaten to overwhelm the server. This sentiment has been expressed very well in [2] where the authors write: "We believe that with quantitative information on the robustness, it will be possible to better determine whether or not the software continues to deal appropriately with risks and threats as their application environment changes." To analyse these problems in large-scale systems we need to use scalable modelling methods.…”

Section: Introductionmentioning

confidence: 99%

“…The use of array notation syntax in PEPA (say, for example, P [2]) indicates a PEPA component array, which is syntactic sugar for P P , which is itself syntactic sugar for P L P when L, the cooperation set, is ∅ (meaning the empty set, as usual).…”

Section: First Model: Server and Clients Onlymentioning

confidence: 99%

Abstract Interpretation of PEPA Models

Gilmore

Hillston

Zoń

2015

Semantics, Logics, and Calculi

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General rightsCopyright for the publications made accessible via the Edinburgh Research Explorer is retained by the author(s) and / or other copyright owners and it is a condition of accessing these publications that users recognise and abide by the legal requirements associated with these rights. Take down policyThe University of Edinburgh has made every reasonable effort to ensure that Edinburgh Research Explorer content complies with UK legislation. If you believe that the public display of this file breaches copyright please contact openaccess@ed.ac.uk providing details, and we will remove access to the work immediately and investigate your claim. Abstract. This paper relates the fluid-flow semantics of the stochastic process algebra PEPA (Performance Evaluation Process Algebra) to the static analysis technique of abstract interpretation. The explanation in the paper is illustrated through the example of a distributed denial of service (DDoS) attack which is being launched against a server. DDoS attacks are mounted by a large population of attackers, who are coordinating and working together in attacking a specific server. The scale of the attack is crucial to its success, but the resulting large number of states in the system makes it difficult to model and analyse using the conventional discrete-state interpretation of PEPA.

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“…The work of D'Argenio inspired the Modest language, which operates with stochastic timed automata (STA) [6] and is supported by the Modest Toolset [16]. Lately, Zeng, Nielson and Nielson proposed the stochastic quality calculus SQC [22] as an intriguing formalism to reason about distributed systems with broadcast communication.…”

Section: Introductionmentioning

confidence: 99%

Schedulers are no Prophets

Hartmanns

Hermanns

Krčál

2015

Semantics, Logics, and Calculi

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Several formalisms for concurrent computation have been proposed in recent years that incorporate means to express stochastic continuous-time dynamics and non-determinism. In this setting, some obscure phenomena are known to exist, related to the fact that schedulers may yield too pessimistic verification results, since current nondeterminism can surprisingly be resolved based on prophesying the timing of future random events. This paper provides a thorough investigation of the problem, and it presents a solution: Based on a novel semantics of stochastic automata, we identify the class of schedulers strictly unable to prophesy, and show a path towards verification algorithms with respect to that class. The latter uses an encoding into the model of stochastic timed automata under arbitrary schedulers, for which model checking tool support has recently become available.

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Stochastic Model Checking of the Stochastic Quality Calculus

Cited by 3 publications

References 16 publications

The Stochastic Quality Calculus

The Stochastic Quality Calculus

Abstract Interpretation of PEPA Models

Schedulers are no Prophets

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