Dynamic power management of complex systems using generalized stochastic Petri nets

  • Authors:
  • Qinru Qiu;Qing Wu;Massoud Pedram

  • Affiliations:
  • Department of Electrical Engineering - Systems, University of Southern California, Los Angeles, CA;Department of Electrical Engineering - Systems, University of Southern California, Los Angeles, CA;Department of Electrical Engineering - Systems, University of Southern California, Los Angeles, CA

  • Venue:
  • Proceedings of the 37th Annual Design Automation Conference
  • Year:
  • 2000

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Abstract

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.