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

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.