PUMA
Istituto di Scienza e Tecnologie dell'Informazione     
Freire A., Macdonald C., Tonellotto N., Ounis I., Cacheda F. A self-adapting latency/power tradeoff model for replicated search engines. In: WSDM'14 - 7th ACM International Conference on Web Search and Data Mining (New York, USA, 24-28 February 2014). Proceedings, pp. 13 - 22. ACM, 2014.
 
 
Abstract
(English)
For many search settings, distributed/replicated search en- gines deploy a large number of machines to ensure efficient retrieval. This paper investigates how the power consump- tion of a replicated search engine can be automatically re- duced when the system has low contention, without com- promising its efficiency. We propose a novel self-adapting model to analyse the trade-off between latency and power consumption for distributed search engines. When query volumes are high and there is contention for the resources, the model automatically increases the necessary number of active machines in the system to maintain acceptable query response times. On the other hand, when the load of the sys- tem is low and the queries can be served easily, the model is able to reduce the number of active machines, leading to power savings. The model bases its decisions on exam- ining the current and historical query loads of the search engine. Our proposal is formulated as a general dynamic decision problem, which can be quickly solved by dynamic programming in response to changing query loads. Thor- ough experiments are conducted to validate the usefulness of the proposed adaptive model using historical Web search traffic submitted to a commercial search engine. Our results show that our proposed self-adapting model can achieve an energy saving of 33% while only degrading mean query com- pletion time by 10 ms compared to a baseline that provisions replicas based on a previous day's traffic.
URL: http://dl.acm.org/citation.cfm?id=2556246&CFID=609747143&CFTOKEN=33326240
DOI: 10.1145/2556195.2556246
Subject Search Engines
Power Consumption
H.3.3 Information Search and Retrieval


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