Dynamic power management of complex systems using generalized stochastic Petri nets

In this paper, we introduce a new technique for modeling and solving the dynamic power management (DPM) problem for systems with complex behavioral characteristics such as concurrency, synchronization, mutual exclusion and conflict. We model a power-managed distributed computing system as a controll...

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Vydáno v:37th Design Automation Conference, 2000 s. 352 - 356
Hlavní autoři: Qiu, Qinru, Wu, Qing, Pedram, Massoud
Médium: Konferenční příspěvek
Jazyk:angličtina
Vydáno: New York, NY, USA ACM 01.01.2000
IEEE
Edice:ACM Conferences
Témata:
Applied computing > Physical sciences and engineering > Engineering
Batteries
Contracts
Energy consumption
Energy management
Engineering management
Hardware > Robustness
Mathematics of computing > Mathematical analysis > Mathematical optimization > Continuous optimization > Linear programming
Mathematics of computing > Probability and statistics > Stochastic processes
Permission
Petri nets
Power system management
Power system modeling
Software and its engineering > Software organization and properties > Software system structures > Software system models > Petri nets
Stochastic systems
Theory of computation > Design and analysis of algorithms > Mathematical optimization > Continuous optimization > Linear programming
ISBN:9781581131871, 1581131879
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Popis
Shrnutí:In this paper, we introduce a new technique for modeling and solving the dynamic power management (DPM) problem for systems with complex behavioral characteristics such as concurrency, synchronization, mutual exclusion and conflict. We model a power-managed distributed computing system as a controllable Generalized Stochastic Petri Net (GSPN) with cost. The obtained GSPN model is automatically converted to an equivalent continuous-time Markov decision process. Given the delay constraints, the optimal power management policy for system components as well as the optimal dispatch policy for requests are calculated by solving a linear programming problem based on the Markov decision process. Experimental results show that the proposed technique can achieve more than 20% power saving compared to other existing DPM techniques.
ISBN:9781581131871
1581131879
DOI:10.1145/337292.337438