Power Shepherd: Application Performance Aware Power Shifting Constantly growing power consumption of data centers is a major concern from environmental and economical reasons. Current approaches to reduce negative consequences of high power consumption focus on limiting the peak power consumption. During high workload periods, power consumption of highly utilized servers is throttled to stay within the power budget. However, the peak power reduction affects performance of hosted applications and thus leads to Quality of Service violations. In this paper, we introduce Power Shepherd, a hierarchical system for application performance aware power shifting. Power Shepherd reduces the data center operational costs by redistributing the available power among applications hosted in the cluster. This is achieved by, assigning server power budgets by the cluster controller, enforcing these power budgets using Running Average Power Limit (RAPL), and prioritizing applications within each server by adjusting the CPU scheduling configuration. We implement a prototype of the proposed solution and evaluate it in a real testbed equipped with power meters and using representative cloud applications. Our experiments show that Power Shepherd has potential to manage a cluster consisting of thousands of servers and limit the increase of operational costs by a significant amount when the cluster power budget is limited and the system is overutilized. Finally, we identify some outstanding challenges regarding model sensitivity and the fact that this approach in its current from is not beneficial to be used in all situations, e.g., when the system is underutilized.

PowerStar: Improving Power Efficiency in Heterogenous Processors for Bursty Workloads with Approximate Computing Modern Data Centers have increasingly adopted heterogeneous processors in their server nodes to maximize power efficiency. However, there are still challenges in how to properly configure these processors such that throughput can be maximized under fluctuating workload while optimizing system power consumption. In this paper, we propose PowerStar, a framework that maximizes power efficiency and reduces the number of reconfigurations needed in heterogeneous processors during periods of fluctuations in job arrival patterns while handling latencycritical workloads. PowerStar is built based on the following two key observations: (i) reconfiguration of heterogeneous processors to add more cores and enable higher performance andor reallocation of computing cores can be costly due to the extra latency involved and the associated energy overheads; (ii) a considerable amount of energy savings can be achieved by keeping the system in most powerefficient configurations capable of absorbing short bursts in job arrivals without needing to reconfigure the system. PowerStar operates by carefully choosing the most powerefficient configurations (states) and judiciously maximizing the state residency through the controlled use of approximate computing, when feasible. We implement PowerStar as a prototype on a 6core ARM big.LITTLE heterogeneous platform and evaluate it with a variety of workloads. Our results show that, compared to a baseline of performancedriven power management policy, our power efficiencyaware PowerStar can reduce the average power by up to 11% under tight QoS (95th percentile latency under 3xc3x97 job execution latency), and can save even higher average power of up to 32% under relaxed QoS (95th percentile latency under 10xc3x97 job execution latency) constraints when compared to the baseline.