Energy-Efficient Query Processing in Web Search Engines

Abstract

Web search engines are composed by thousands of query processing nodes, i.e., servers dedicated to process user queries. Such many servers consume a significant amount of energy, mostly accountable to their CPUs, but they are necessary to ensure low latencies, since users expect sub-second response times (e.g., 500 ms). However, users can hardly notice response times that are faster than their expectations. Hence, we propose the Predictive Energy Saving Online Scheduling Algorithm ( <inline-formula><tex-math notation="LaTeX">$\sf{PESOS}$</tex-math> </inline-formula> ) to select the most appropriate CPU frequency to process a query on a per-core basis. <inline-formula><tex-math notation="LaTeX"> $\sf{PESOS}$</tex-math> </inline-formula> aims at process queries by their deadlines, and leverage high-level scheduling information to reduce the CPU energy consumption of a query processing node. <inline-formula><tex-math notation="LaTeX"> $\sf{PESOS}$</tex-math> </inline-formula> bases its decision on query efficiency predictors, estimating the processing volume and processing time of a query. We experimentally evaluate <inline-formula><tex-math notation="LaTeX">$\sf{PESOS}$ </tex-math></inline-formula> upon the TREC ClueWeb09B collection and the MSN2006 query log. Results show that <inline-formula><tex-math notation="LaTeX"> $\sf{PESOS}$</tex-math> </inline-formula> can reduce the CPU energy consumption of a query processing node up to <inline-formula> <tex-math notation="LaTeX">${\sim}$</tex-math></inline-formula> 48 percent compared to a system running at maximum CPU core frequency. <inline-formula><tex-math notation="LaTeX">$\sf{PESOS}$</tex-math> </inline-formula> outperforms also the best state-of-the-art competitor with a <inline-formula><tex-math notation="LaTeX">${\sim}$</tex-math> </inline-formula> 20 percent energy saving, while the competitor requires a fine parameter tuning and it may incurs in uncontrollable latency violations.