Swan

Abstract

The service quality of web search depends considerably on the request tail latency from Index Serving Nodes (ISNs), prompting data centers to operate them at low utilization and wasting server power. ISNs can be made more energy efficient utilizing Dynamic Voltage and Frequency Scaling (DVFS) or sleep states techniques to take advantage of slack in latency of search queries. However, state-of-the-art frameworks use a single distribution to predict a request's service time and select a high percentile tail latency to derive the CPU's frequency or sleep states. Unfortunately, this misses plenty of energy saving opportunities. In this paper, we develop a simple linear regression predictor to estimate each individual search request's service time, based on the length of the request's posting list. To use this prediction for power management, the major challenge lies in reducing miss rates for deadlines due to prediction errors, while improving energy efficiency. We present Swan, a two-Step poWer mAnagement for distributed search eNgines. For each request, Swan selects an initial, lower frequency to optimize power, and then appropriately boosts the CPU frequency just at the right time to meet the deadline. Additionally, we re-configure the time instant for boosting frequency, when a critical request arrives and avoid deadline violations. Swan is implemented on the widely-used Solr search engine and evaluated with two representative, large query traces. Evaluations show Swan outperforms state-of-the-art approaches, saving at least 39% CPU power on average.