2017
Journal article  Restricted

Energy-efficient query processing in web search engines

Catena M., Tonellotto N.

Computational Theory and Mathematics  Energy consumption  web search engines  CPU dynamic voltage and frequency scaling  Computer Science Applications  Information Systems 

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 ( PESOS) to select the most appropriate CPU frequency to process a query on a per-core basis. PESOS aims at process queries by their deadlines, and leverage high-level scheduling information to reduce the CPU energy consumption of a query processing node. PESOS bases its decision on query efficiency predictors, estimating the processing volume and processing time of a query. We experimentally evaluate PESOS upon the TREC ClueWeb09B collection and the MSN2006 query log. Results show that PESOS can reduce the CPU energy consumption of a query processing node up to similar to 48 percent compared to a system running at maximum CPU core frequency. PESOS outperforms also the best state-of-the-art competitor with a similar to 20 percent energy saving, while the competitor requires a fine parameter tuning and it may incurs in uncontrollable latency violations.

Source: IEEE transactions on knowledge and data engineering (Print) 29 (2017): 1412–1425. doi:10.1109/TKDE.2017.2681279

Publisher: Institute of Electrical and Electronics Engineers,, New York, NY , Stati Uniti d'America


Metrics



Back to previous page
BibTeX entry
@article{oai:it.cnr:prodotti:384712,
	title = {Energy-efficient query processing in web search engines},
	author = {Catena M. and Tonellotto N.},
	publisher = {Institute of Electrical and Electronics Engineers,, New York, NY , Stati Uniti d'America},
	doi = {10.1109/tkde.2017.2681279},
	journal = {IEEE transactions on knowledge and data engineering (Print)},
	volume = {29},
	pages = {1412–1425},
	year = {2017}
}