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Computing systems, ranging from small battery-operated embedded systems to more complex general purpose systems, are designed to satisfy various computation demands in some acceptable time. In doing so, the system is responsible for scheduling jobs/requests in a dynamic fashion. In addition, with power consumption recently becoming a critical issue, most systems are also responsible for their own power management. In some rare cases, the exact arrival time and execution time of jobs/requests is known, leading to precise scheduling algorithms and power management schemes. However, more often than not, there is no a-priori knowledge of the workload. This work evaluates dynamic voltage scaling (DVS) policies for power management in systems with unpredictable workloads. A clear winner is identified, a policy that reduces the energy consumption one order of magnitude compared to no power management and up to 40% (in real-life traces) and 50% (in synthetic workloads) compared to the second-best evaluated scheme.