Browsing by Author "Junqueira, F. P."

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    Energy-price-driven query processing in multi-center web search engines
    (IEEE, 2011-07) Kayaaslan, Enver; Cambazoglu, B. B.; Blanco, R.; Junqueira, F. P.; Aykanat, Cevdet
    Concurrently processing thousands of web queries, each with a response time under a fraction of a second, necessitates maintaining and operating massive data centers. For large-scale web search engines, this translates into high energy consumption and a huge electric bill. This work takes the challenge to reduce the electric bill of commercial web search engines operating on data centers that are geographically far apart. Based on the observation that energy prices and query workloads show high spatio-temporal variation, we propose a technique that dynamically shifts the query workload of a search engine between its data centers to reduce the electric bill. Experiments on real-life query workloads obtained from a commercial search engine show that significant financial savings can be achieved by this technique.
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    A five-level static cache architecture for web search engines
    (Elsevier Ltd, 2012) Ozcan, R.; Altingovde, I. S.; Cambazoglu, B. B.; Junqueira, F. P.; Ulusoy, Özgür
    Caching is a crucial performance component of large-scale web search engines, as it greatly helps reducing average query response times and query processing workloads on backend search clusters. In this paper, we describe a multi-level static cache architecture that stores five different item types: query results, precomputed scores, posting lists, precomputed intersections of posting lists, and documents. Moreover, we propose a greedy heuristic to prioritize items for caching, based on gains computed by using items' past access frequencies, estimated computational costs, and storage overheads. This heuristic takes into account the inter-dependency between individual items when making its caching decisions, i.e.; after a particular item is cached, gains of all items that are affected by this decision are updated. Our simulations under realistic assumptions reveal that the proposed heuristic performs better than dividing the entire cache space among particular item types at fixed proportions. © 2010 Elsevier Ltd. All rights reserved.