The problem of power management for an embedded system is to reduce 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 where the algorithm must operate without access to the complete data set or its characteristics. In this paper, we present online algorithms to manage power for embedded systems and provide experimental analysis to back up the theoretical results.Specifically, this paper makes four contributions. We propose an optimal online algorithm for power management. We present analysis of algorithmic efficiency using a technique called competitive analysis, which is particularly suitable for online algorithms. Using this analysis technique, we develop a lower bound for the non-adaptive version of the power management problem and show that our algorithm achieves this lower bound. Next, we explore adaptive algorithms that try to shut down the system based on historical data. We provide a lower bound for any algorithm that uses adaptive methods to manage power. We also propose an algorithm that is independent of the input data distribution, practical and usable in both hardware and software systems with guaranteed performance. Finally, we compare these algorithms with previously proposed heuristics both theoretically and experimentally. For the experiments, we model the disk drive of a laptop computer as an embedded system. The results show that the proposed algorithms perform well in practice with guaranteed bounds on their performance. Further, This paper conclusively demonstrates that to implement aggressive power management techniques for power critical subsystems, designers will have to commit greater resources such as dedicated registers and ALU units.