A survey of design techniques for system-level dynamic power management
IEEE Transactions on Very Large Scale Integration (VLSI) Systems - Special section on low-power electronics and design
Bounded-parameter Markov decision process
Artificial Intelligence
Dynamic Power Management for Nonstationary Service Requests
IEEE Transactions on Computers
Bounded Parameter Markov Decision Processes
ECP '97 Proceedings of the 4th European Conference on Planning: Recent Advances in AI Planning
Modeling multiple IP traffic streams with rate limits
IEEE/ACM Transactions on Networking (TON)
Hierarchical Adaptive Dynamic Power Management
Proceedings of the conference on Design, automation and test in Europe - Volume 1
Proportional differentiation: a scalable QoS approach
IEEE Communications Magazine
Policy optimization for dynamic power management
IEEE Transactions on Computer-Aided Design of Integrated Circuits and Systems
Stochastic modeling of a power-managed system-construction and optimization
IEEE Transactions on Computer-Aided Design of Integrated Circuits and Systems
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Dynamic Power Management (DPM) is an effective power reduction technique to dynamically control power state of system components. Although there are already a lot of papers on DPM, few of them present robust and adaptive solution to handle the inherent time varying behavior of real world system. In this paper, we propose techniques for targeting DPM on the time varying behaviors. In our approach, we apply efficient policy optimization method to generate online power management policy to achieve adaptiveness. Moreover, in order to handle the small scale varying behavior (perturbation) that tends to significantly degrade the performance of DPM, we apply the Bounded Parameter Markov Decision Process and Interval Value Iteration to derive robust policy tolerant to perturbations. Simulation results show that the proposed techniques are effective.