Formal Methods for Dynamic Power Management
Proceedings of the 2003 IEEE/ACM international conference on Computer-aided design
Probabilistic model checking in practice: case studies with PRISM
ACM SIGMETRICS Performance Evaluation Review
Model checking expected time and expected reward formulae with random time bounds
Computers & Mathematics with Applications
Quantitative Analysis With the Probabilistic Model Checker PRISM
Electronic Notes in Theoretical Computer Science (ENTCS)
Abstract interpretation for worst and average case analysis
Program analysis and compilation, theory and practice
IEEE Transactions on Very Large Scale Integration (VLSI) Systems
SOC dynamic power management using artificial neural network
ICNC'06 Proceedings of the Second international conference on Advances in Natural Computation - Volume Part I
A methodology based on formal methods for predicting the impact of dynamic power management
SFM-Moby'05 Proceedings of the 5th international conference on Formal Methods for the Design of Computer, Communication, and Software Systems: mobile computing
Proceedings of the 3rd International Conference on Future Energy Systems: Where Energy, Computing and Communication Meet
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We have shown in the past that competitive analysis based power management strategies can be automatically analyzed for proving competitive bounds and for validating power management strategies using the SMV model checker. We show that stochastic modelling based strategies for power management can similarly be automated for computing optimal strategies. Further these can be analyzed for finding system parameters for satisfying probabilistic constraints. Effects of any changes in probabilistic assumptions can be easily analyzed without expensive and time consuming simulations. We demonstrate our methodology using the probabilistic model checker PRISM. We model the system using a continuous-time Markov chain, and compute strategies under varying requirements for performance. We also prove probabilistic properties of strategies using PRISM, which gives insight into individual strategies and pragmatics of their implementations. We also show the effects of changing probabilistic assumptions computed by our method and compare the results with other stochastic analysis based methods, and show that we obtain similar results in a uniform framework of probabilistic model checking.