Operating system concepts (2nd ed.)
Operating system concepts (2nd ed.)
Self-adjusting binary search trees
Journal of the ACM (JACM)
A survey of power estimation techniques in VLSI circuits
IEEE Transactions on Very Large Scale Integration (VLSI) Systems - Special issue on low-power design
A survey of optimization techniques targeting low power VLSI circuits
DAC '95 Proceedings of the 32nd annual ACM/IEEE Design Automation Conference
Power minimization in IC design: principles and applications
ACM Transactions on Design Automation of Electronic Systems (TODAES)
IEEE Transactions on Very Large Scale Integration (VLSI) Systems
A predictive system shutdown method for energy saving of event-driven computation
ICCAD '97 Proceedings of the 1997 IEEE/ACM international conference on Computer-aided design
Policy optimization for dynamic power management
DAC '98 Proceedings of the 35th annual Design Automation Conference
System level online power management algorithms
DATE '00 Proceedings of the conference on Design, automation and test in Europe
Dynamic Power Management: Design Techniques and CAD Tools
Dynamic Power Management: Design Techniques and CAD Tools
SODA '03 Proceedings of the fourteenth annual ACM-SIAM symposium on Discrete algorithms
Online strategies for dynamic power management in systems with multiple power-saving states
ACM Transactions on Embedded Computing Systems (TECS)
Formal Methods for Dynamic Power Management
Proceedings of the 2003 IEEE/ACM international conference on Computer-aided design
Algorithmic problems in power management
ACM SIGACT News
ACM Transactions on Algorithms (TALG)
Energy Smart Management of Scientific Data
SSDBM 2009 Proceedings of the 21st International Conference on Scientific and Statistical Database Management
IEEE Transactions on Very Large Scale Integration (VLSI) Systems
Assertive dynamic power management (AsDPM) strategy for globally scheduled RT multiprocessor systems
PATMOS'09 Proceedings of the 19th international conference on Integrated Circuit and System Design: power and Timing Modeling, Optimization and Simulation
An online algorithm optimally self-tuning to congestion for power management problems
WAOA'11 Proceedings of the 9th international conference on Approximation and Online Algorithms
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A power management algorithm for an embedded system reduces system level power dissipation by shutting off parts of the system when they are not being used and turning them back on when they are required. Algorithms for this problem are online in nature since they must operate without knowledge of the arrival time or service requirements of future requests. In this paper, we present online algorithms to manage power for embedded systems. We perform an empirical analysis of these algorithms and give theoretical justification for the empirical results. Effective power management strategies have an adverse impact on the latency of the system for which the strategy is designed. Typically, the more aggressive the power management scheme, the greater the increase in the latency of the system. In this paper, we prove an upper bound on the additional latency of the system introduced by power management strategies. Moreover, we show that this upper bound occurs each time the system is shutdown and hence is an important system design parameter.In addition, service time and latencies have an effect on power management strategies since they alter the length and occurrences of idle periods which. We study this phenomenon experimentally, by modeling the disk drive of a laptop computer as an embedded system. The results show that if service times of arriving requests are modeled, the relative performance of algorithms can change leading to non-adaptive algorithms performing better than adaptive ones. We compare the performance of adaptive and non-adaptive power management algorithms. In particular, our experimental results show that an "immediate" shutdown strategy that shuts down the system whenever it encounters an idle period performs surprising better than sophisticated adaptive algorithms suggested in the literature. We provide an analytical explanation for the effectiveness of power management strategies.