Prefetching query results and its impact on search engines

Jonassen, Simon; Cambazoğlu, B. Barla; Silvestri, Fabrizio

doi:10.1145/2348283.2348368

Cited by 33 publications

(11 citation statements)

References 26 publications

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Section: Discussionmentioning

confidence: 99%

Query Scheduling Techniques and Power-Latency Trade-off Model for Large-Scale Search Engines

Freire

2015

SIGIR Forum

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AcknowledgmentsThis path would not have been possible without the help of my supervisor, FidelCacheda, who gave me the opportunity of diving again into the Information Retrieval field, after an unforgettable start in the Information Retrieval Lab. Thank you for being such a great motivating person, for guiding and advising me in the best way and, above all, for always letting me choose the next step.All my gratitude to all the colleagues that have shared with me great moments in the Telematic Engineering Lab. Thank you for making our office a warm place to spend lots of hours and for building such a great friendship even out of our blind walls.I would like to acknowledge Iadh Ounis and the Information Retrieval Group from the University of Glasgow. They gave me the opportunity of staying three months in 2012 in one of the leading groups in IR, where I put in touch with an impeccable way of work. My special gratitude goes to Craig Macdonald: thank you for becoming my best teacher and reference during all my PhD. I can't ever forget some of your encouraging words that became my motto: Don't think "is this sufficient?" but "how can we do better". All this work would not have being possible without your valuable help. A special gratitude also for Silvia Lorenzo Freire, for sharing with me her huge knowledge and offering me her useful help. Vorrei ringraziare in modo particolare a tutti i membri del HighI can not forget Roi Blanco, who was discreetly present at every milestone of my career. Thank you also for opening me the next opportunity of learning, I will make the most of it.A great deal of gratitude is due to all the people that have walked with me during this period. Your encouraging and warm-hearted words were the best incentive to finish this work. Those who kept me out of this thesis, sharing awesome moments and messages, have also done a good job. AbstractWeb search engines have to deal with a rapid increase of information, demanded by high incoming query traffic. This situation has driven companies to build geographically distributed data centres housing thousands of computers, consuming enormous amounts of electricity and requiring a huge infrastructure around. At this scale, even minor efficiency improvements result in large financial savings.This thesis represents a novel contribution to query scheduling and power consumption state-of-the-art, by assisting large-scale data centres to build more efficient search engines.On the one hand, this thesis proposes new scheduling techniques to decrease the response time of queries, by estimating the server that will be idle soonest.On the other hand, this thesis defines a simple mathematical model that establishes a threshold between the power and latency of a search engine. Using historical and current data, the model estimates the incoming query traffic and automatically increases/decreases the necessary number of active machines in the system. We achieve high energy savings during the whole day, without degrading the latency.Our experiments have attested th...

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Section: Discussionmentioning

confidence: 99%

Query Scheduling Techniques and Power-Latency Trade-off Model for Large-Scale Search Engines

Freire

2015

SIGIR Forum

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“…There are advantages to such a scenario, because during off-peak times standby query servers may be re-purposed to other offline tasks, such as indexing, ads/recommendations generation, and precaching of result lists [15]. For future work, we will consider a more complex scenario where servers are fully powered down when not required, but incur delay on startup.…”

Section: Discussionmentioning

confidence: 99%

A self-adapting latency/power tradeoff model for replicated search engines

Freire

Macdonald

Tonellotto

et al. 2014

Proceedings of the 7th ACM International Conference on Web Search and Data Mining

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For many search settings, distributed/replicated search engines deploy a large number of machines to ensure efficient retrieval. This paper investigates how the power consumption of a replicated search engine can be automatically reduced when the system has low contention, without compromising 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 system 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 examining 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. Thorough 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 completion time by 10 ms compared to a baseline that provisions replicas based on a previous day's traffic.

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“…In computer architecture design, prefetching is usually employed to request instructions that are anticipated to be executed in the future and place them in the CPU cache. For information retrieval, query prefetching typically assumes a probabilistic model, e.g., considering temporal or spatial features [6,9]. However, there have been no attempts to prefetch RDF data based on the structure of sequential related Sparql queries within and across query sessions.…”

Section: Semantic Caching and Prefetchingmentioning

confidence: 99%

Caching and Prefetching Strategies for SPARQL Queries

Lorey

Naumann

2013

Advanced Information Systems Engineering

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Linked Data repositories offer a wealth of structured facts, useful for a wide array of application scenarios. However, retrieving this data using Sparql queries yields a number of challenges, such as limited endpoint capabilities and availability, or high latency for connecting to it. To cope with these challenges, we argue that it is advantageous to cache data that is relevant for future information needs. However, instead of only retaining results of previously issued queries, we aim at retrieving data that is potentially interesting for subsequent requests in advance. To this end, we present different methods to modify the structure of a query so that the altered query can be used to retrieve additional related information. We evaluate these approaches by applying them to requests found in real-world Sparql query logs.

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Prefetching query results and its impact on search engines

Cited by 33 publications

References 26 publications

Query Scheduling Techniques and Power-Latency Trade-off Model for Large-Scale Search Engines

Query Scheduling Techniques and Power-Latency Trade-off Model for Large-Scale Search Engines

A self-adapting latency/power tradeoff model for replicated search engines

Caching and Prefetching Strategies for SPARQL Queries

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