Latency effects of system level power management algorithms

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.