Improve query response time and reduce CPU cost in web search

N Devi, P.Radhika Krupalini

Abstract


We suggest the Predictive Energy Saving Online Scheduling Algorithm (PESOS) to choice the greatest suitable CPU frequency to procedure a query on a per-core basis. PESOS goal at procedure queries by their limits, and leverage high-level preparation info to decrease the CPU energy ingesting of a query dispensation node. PESOS base its result on inquiry efficacy predictors, guessing the meting out volume and meting out time of a query. We experimentally gauge PESOS upon the TREC ClueWeb09B collection and the MSN2006 query log. PESOS outpace also the best state-of-the-art entrant with a 20% oomph saving, while the player requires a fine restriction fine-tuning and it may invite in wild potential abuses.


References


L. A. Barroso, J. Clidaras, and U. H¨olzle, The Datacenter as a Computer: An Introduction to the Design of Warehouse-Scale Machines, 2nd ed. Morgan & Claypool Publishers, 2013.

I. Arapakis, X. Bai, and B. B. Cambazoglu, “Impact of response latency on user behavior in web search,” in Proc. SIGIR, 2014,pp. 103–112.

U.S. Department of Energy, “Quick start guide to increase data center energy efficiency,” 2009. [Online]. Available: http://goo.gl/ovDP26

The Climate Group for the Global e-Sustainability Initiative, “Smart 2020: Enabling the low carbon economy in the information age,” 2008. [Online]. Available: http://goo.gl/w5gMXa

European Commission – Joint Research Centre, “The European Code of Conduct for Energy Efficiency in Data Centre.” [Online].Available: http://goo.gl/wmqYLQ

U.S. Department of Energy, “Best Practices Guide for Energy-Efficient Data Center Design.” [Online]. Available:http://goo.gl/pikFFv

D. C. Snowdon, S. Ruocco, and G. Heiser, “Power Management and Dynamic Voltage Scaling: Myths and Facts,” in Proc. Of Workshop on Power Aware Real-time Computing, 2005.

The Linux Kernel Archives, “Intel P-State driver.”[Online].Available: https://goo.gl/w9JyBa

D. Brodowski, “CPU frequency and voltage scaling code in the Linux kernel.” [Online]. Available: https://goo.gl/QSkft2

C. Macdonald, N. Tonellotto, and I. Ounis, “Learning to predict response times for online query scheduling,” in Proc. SIGIR,2012, pp. 621–630.

M. Jeon, S. Kim, S.-w. Hwang, Y. He, S. Elnikety, A. L. Cox, and S. Rixner, “Predictive parallelization: Taming tail latencies in web search,” in Proc. SIGIR, 2014, pp. 253–262.

Matteo Catena and Nicola Tonellotto, Energy-Efficient Query processing in Web Search Engines,2017


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