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Saving energy for latency-critical applications like web search can be challenging because of their strict tail latency constraints. State-of-the-art power management frameworks use Dynamic Voltage and Frequency Scaling (DVFS) and Sleep states techniques to slow down the request processing and finish the search just-in-time. However, accurately predicting the compute demand of a request can be difficult. In this paper, we present Gemini, a novel power management framework for latency- critical search engines. Gemini has two unique features to capture the per query service time variation. First, at light loads without request queuing, a two-step DVFS is used to manage the CPU power. Our two-step DVFS selects the initial CPU frequency based on the query specific service time prediction and then judiciously boosts the initial frequency at the right time to catch-up to the deadline. The determination of boosting time further relies on estimating the error in the prediction of individual query’s service time. At high loads, where there is request queuing, only the current request being executed and the critical request in the queue adopt a two-step DVFS. All the other requests in-between use the same frequency to reduce the frequency transition overhead. Second, we develop two separate neural network models, one for predicting the service time and the other for the error in the prediction. The combination of these two predictors significantly improves the power saving and tail latency results of our two-step DVFS. Gemini is implemented on the Solr search engine. Evaluations on three representative query traces show that Gemini saves 41% of the CPU power, and is better than other state-of-the-art techniques. 

Power management in data centers is challenging because of fluctuating workloads and strict task completion time requirements. Recent resource provisioning systems, such as Borg and RC-Informed, pack tasks on servers to save power. However, current power optimization frameworks based on packing leave very little headroom for spikes, and the task completion times are compromised. In this paper, we design Goldilocks, a novel resource provisioning system for optimizing both power and task completion time by allocating tasks to servers in groups. Tasks hosted in containers are grouped together by running a graph partitioning algorithm. Containers communicating frequently are placed together, which improves the task completion times. We also leverage new findings on power consumption of modern- day servers to ensure that their utilizations are in a range where they are power-proportional. Both testbed implementation measurements and large-scale trace-driven simulations prove that Goldilocks outperforms all the previous works on data center power saving. Goldilocks saves power by 11.7%-26.2% depending on the workload, whereas the best of the implemented alternatives, Borg, saves 8.9%-22.8%. The energy per request for the Twitter content caching workload in Goldilocks is only 33% of RC-Informed. Finally, the best alternative in terms of task completion time, E-PVM, has 1.17-3.29 times higher task completion times than Goldilocks across different workloads.