Model Checking the Multi-Formalism Language FIGARO

Khan, Shahid; Volk, Matthias; Katoen, Joost-Pieter; Braibant, Alexis; Bouissou, Marc

doi:10.1109/dsn48987.2021.00056

Cited by 3 publications

(2 citation statements)

References 37 publications

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“…With regard to multiformalism modelling, some interesting trends [30,31] refer to multiparadigm modelling, including discrete-event, continuous-time, or agent-based modelling, to describe different aspects of a system or process and (ii) model validation and verification, which involves ensuring that models are accurate and reliable by comparing them to real-world data, running simulations, and performing sensitivity analyses.…”

Section: Discussionmentioning

confidence: 99%

A Multiformalism-Based Model for Performance Evaluation of Green Data Centres

2023

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Although the coexistence of ARM and INTEL technologies in green data centres is technically feasible, significant challenges exist that must be addressed. These challenges stem from the differences in instruction sets and power consumption between the two processor architectures. While ARM processors are known for their energy efficiency, INTEL processors tend to consume more power. Consequently, evaluating the performance of hybrid architectures can be a complex task. The contributions of this article consist of (i) a multiformalism-based model of a data centre, providing a natural and convenient approach to the specification process and performance analysis of a realistic scenario and (ii) a review of the performance indices, including the choice of one architecture over another, power consumption, the response time, and request loss, according to different policies. As a result, the model aims to address issues such as system underutilization and the need to estimate the optimal workload balance, thereby providing an effective solution for evaluating the performance of hybrid hardware architectures.

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Section: Discussionmentioning

confidence: 99%

A Multiformalism-Based Model for Performance Evaluation of Green Data Centres

2023

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“…• All the above cited tools are "industry proof" tools, used in real studies of complex systems such as nuclear power plants, telecommunication and electrical networks... KB3 is commercially available under the name RiskSpectrum ModelBuilder. A new tool, still a prototype, is available to process FIGARO Markovian models: it is based on the STORM probabilistic model checker, cf [50,49]. This open source tool, called STORM-Figaro, is now available on github.…”

Section: Figaro Workbenchmentioning

confidence: 99%

ARCH-COMP22 Category Report: Stochastic Models

Abate¹,

Blom²,

Delicaris³

et al.

EPiC Series in Computing

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This report presents the results of a friendly competition for formal verification and policy synthesis of stochastic models. It also introduces new benchmarks and their properties within this category and recommends next steps for this category towards next year’s edition of the competition. In comparison with tools on non-probabilistic models, the tools for stochastic models are at the early stages of development that do not allow full competition on a standard set of benchmarks. We report on an initiative to collect a set of minimal benchmarks that all such tools can run, thus facilitating the comparison between efficiency of the implemented techniques. The friendly competition took place as part of the workshop Applied Verification for Continuous and Hybrid Systems (ARCH) in Summer 2022.

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