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Conference Papers Year : 2021

Modeling and Analysis for Energy-Driven Computing using Statistical Model-Checking

Abdoulaye Gamatié
Gilles Sassatelli

Abstract

Energy-driven computing is a recent paradigm that promotes energy harvesting as an alternative solution to conventional power supply systems. A crucial challenge in that context lies in the dimensioning of system resources w.r.t. energy harvesting conditions while meeting some given timing QoS requirements. Existing simulation and debugging tools do not make it possible to clearly address this issue. This paper defines a generic modeling and analysis framework to support the design exploration for energy-driven computing. It uses stochastic hybrid automata and statistical model-checking. It advocates a distributed system design, where heterogeneous nodes integrate computing and harvesting components and support inter-node energy transfer. Through a simple case-study, the paper shows how this framework addresses the aforementioned design challenge in a flexible manner and helps in reducing energy storage requirements. Index Terms-Stochastic hybrid automata, energy-driven computing, statistical model-checking, energy harvesting and buffering
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Dates and versions

lirmm-03143143 , version 1 (16-02-2021)

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Abdoulaye Gamatié, Gilles Sassatelli, Marius Mikučionis. Modeling and Analysis for Energy-Driven Computing using Statistical Model-Checking. DATE 2021 - 24th Design, Automation and Test in Europe Conference and Exhibition, Feb 2021, Grenoble (Virtual), France. pp.980-985, ⟨10.23919/DATE51398.2021.9474224⟩. ⟨lirmm-03143143⟩
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