Quantitative Multi-objective Verification for Probabilistic Systems

Forejt, Vojtěch; Kwiatkowska, Marta; Norman, Gethin; Parker, David; Qu, Hongyang

doi:10.1007/978-3-642-19835-9_11

Cited by 94 publications

(155 citation statements)

References 22 publications

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“…We have discussed recent progress in the development of automated compositional verification techniques for probabilistic systems, focusing on the assumeguarantee framework of [29,21] for probabilistic automata. We also described how the verification process can be automated further using learning-based generation of the assumptions needed to apply assume-guarantee proof rules and described some recent improvements and extensions to this work.…”

Section: Discussionmentioning

confidence: 99%

“…One is to extend our techniques for learning probabilistic assumptions to the assume-guarantee framework in [21], which additionally includes ω-regular and expected reward properties. Here, the ω-regular language learning algorithms of [19,8] may provide a useful starting point.…”

Section: Discussionmentioning

confidence: 99%

“…probabilistic linear-time) properties across the set of adversaries of a PA. Multi-objective probabilistic model checking (see e.g. [18,21]) is a key ingredient in the implementation of the probabilistic assume-guarantee framework discussed in this paper. Algorithmically, probabilistic model checking for PAs reduces in most cases to a combination of graph-based algorithms and numerical computation [16,1].…”

Section: Modelling and Verification Of Probabilistic Systemsmentioning

confidence: 99%

“…This includes cases where non-compositional verification is either slower or infeasible. In recent work [21], this framework is extended to permit the use of more expressive assumptions (and properties): quantitative multi-objective properties, essentially Boolean combinations of probabilistic safety, ω-regular and expected total reward properties. An example of an expressible assumption is "with probability 1, component M i always eventually sends a message and the expected time to do so is at most 10 time-units."…”

Section: Compositional Reasoning For Probabilistic Systemsmentioning

confidence: 99%

“…In this paper, we discuss some recent developments in the area of automated compositional verification techniques for probabilistic systems, focusing on assume-guarantee verification techniques [29,21] for probabilistic automata [35,36], a natural model for compositional reasoning. When aiming to develop fully automated verification techniques, an important question that arises is how to devise suitable assumptions about system components.…”

Section: Introductionmentioning

confidence: 99%

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Automated Learning of Probabilistic Assumptions for Compositional Reasoning

Kwiatkowska

Parker

2011

Fundamental Approaches to Software Engineering

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Abstract. Probabilistic verification techniques have been applied to the formal modelling and analysis of a wide range of systems, from communication protocols such as Bluetooth, to nanoscale computing devices, to biological cellular processes. In order to tackle the inherent challenge of scalability, compositional approaches to verification are sorely needed. An example is assume-guarantee reasoning, where each component of a system is analysed independently, using assumptions about the other components that it interacts with. We discuss recent developments in the area of automated compositional verification techniques for probabilistic systems. In particular, we describe techniques to automatically generate probabilistic assumptions that can be used as the basis for compositional reasoning. We do so using algorithmic learning techniques, which have already proved to be successful for the generation of assumptions for compositional verification of non-probabilistic systems. We also present recent improvements and extensions to this work and survey some of the promising potential directions for further research in this area.

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Section: Discussionmentioning

confidence: 99%

Section: Discussionmentioning

confidence: 99%

Section: Modelling and Verification Of Probabilistic Systemsmentioning

confidence: 99%

Section: Compositional Reasoning For Probabilistic Systemsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Automated Learning of Probabilistic Assumptions for Compositional Reasoning

Kwiatkowska

Parker

2011

Fundamental Approaches to Software Engineering

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A review of microservices autoscaling with formal verification perspective

2022

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The process of scaling microservices is a challenging task, especially in maintaining optimum resource provisioning while respecting QoS constraints and SLA.Many research works have proposed autoscaling approaches for microservices, however, less likely concerned with the correctness guarantee of the proposed algorithms. Hence, it is significant to gather and summarize these approaches to foster future innovation. Meanwhile, a few reviews have been published concerning microservices from different aspects. Therefore, our review complements the existing by focusing on autoscaling with verification perspectives. This study highlights the recent contributions in three inter-related main topics that were published within the year 2017 to 2022, namely, microservice, verification, and autoscaling. Due to limited resources on verification for microservice autoscaling, we widen the perspective by considering the verification for autoscaling in cloud-based systems. Based on our findings, we found that the formal method is not a new thing in verifying the autoscaling policies in cloud-based systems, and one recent study that implements the formal method in the microservices area has been identified. Apart from the autoscaling techniques, we have also determined several factors that have been a concern in scaling the microservices as well as the relatable metrics. Meanwhile, from a verification perspective, we identified that probabilistic model checking is the common formal verification technique used to verify microservices and cloud autoscaling. Finally, we recommend open challenges from two perspectives which highlight the verification for existing microservice autoscaling and verification for ML-based microservice autoscaling.

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Farkas Certificates and Minimal Witnesses for Probabilistic Reachability Constraints

Funke

Jantsch

Baier

2020

Lecture Notes in Computer Science

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This paper introduces Farkas certificates for lower and upper bounds on minimal and maximal reachability probabilities in Markov decision processes (MDP), which we derive using an MDP-variant of Farkas' Lemma. The set of all such certificates is shown to form a polytope whose points correspond to witnessing subsystems of the model and the property. This allows translating the problem of finding minimal witnesses to the problem of finding vertices with a maximal number of zeros. While computing such vertices is computationally hard in general, we derive new heuristics from our formulations that exhibit competitive performance compared to state-of-the-art techniques and apply to more situations. As an argument that asymptotically better algorithms cannot be hoped for, we show that the decision version of finding minimal witnesses is NP-complete even for acyclic Markov chains.

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Quantitative Multi-objective Verification for Probabilistic Systems

Cited by 94 publications

References 22 publications

Automated Learning of Probabilistic Assumptions for Compositional Reasoning

Automated Learning of Probabilistic Assumptions for Compositional Reasoning

A review of microservices autoscaling with formal verification perspective

Farkas Certificates and Minimal Witnesses for Probabilistic Reachability Constraints

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