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Probabilistic model checking of perturbed MDPs with applications to cloud computing

Published: 21 August 2017 Publication History

Abstract

Probabilistic model checking is a formal verification technique that has been applied successfully in a variety of domains, providing identification of system errors through quantitative verification of stochastic system models. One domain that can benefit from probabilistic model checking is cloud computing, which must provide highly reliable and secure computational and storage services to large numbers of mission-critical software systems.
For real-world domains like cloud computing, external system factors and environmental changes must be estimated accurately in the form of probabilities in system models; inaccurate estimates for the model probabilities can lead to invalid verification results. To address the effects of uncertainty in probability estimates, in previous work we have developed a variety of techniques for perturbation analysis of discrete- and continuous-time Markov chains (DTMCs and CTMCs). These techniques determine the consequences of the uncertainty on verification of system properties. In this paper, we present the first approach for perturbation analysis of Markov decision processes (MDPs), a stochastic formalism that is especially popular due to the significant expressive power it provides through the combination of both probabilistic and nondeterministic choice.
Our primary contribution is a novel technique for efficiently analyzing the effects of perturbations of model probabilities on verification of reachability properties of MDPs. The technique heuristically explores the space of adversaries of an MDP, which encode the different ways of resolving the MDP's nondeterministic choices. We demonstrate the practical effectiveness of our approach by applying it to two case studies of cloud systems.

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cover image ACM Conferences
ESEC/FSE 2017: Proceedings of the 2017 11th Joint Meeting on Foundations of Software Engineering
August 2017
1073 pages
ISBN:9781450351058
DOI:10.1145/3106237
Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

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Published: 21 August 2017

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Author Tags

  1. Markov decision processes
  2. cloud computing
  3. perturbation analysis
  4. probabilistic model checking
  5. uncertainty

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Overall Acceptance Rate 112 of 543 submissions, 21%

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  • (2021)Parametric Spatio-temporal Modeling and Safety Verifying for T2T-CBTC Systems2021 International Symposium on Theoretical Aspects of Software Engineering (TASE)10.1109/TASE52547.2021.00032(71-78)Online publication date: Aug-2021
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