A DFT-Based Running Time Prediction Algorithm for Web Queries
Web search engines are built from components capable of processing large amounts of user queries per second in a distributed way. Among them, the index service computes the topk documents that best match each incoming query by means of a document ranking operation. To achieve high performance, dynam...
| Autores: | , , |
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| Tipo de recurso: | artículo |
| Estado: | Versión publicada |
| Fecha de publicación: | 2021 |
| País: | Argentina |
| Institución: | Consejo Nacional de Investigaciones Científicas y Técnicas |
| Repositorio: | CONICET Digital (CONICET) |
| Idioma: | inglés |
| OAI Identifier: | oai:ri.conicet.gov.ar:11336/150245 |
| Acceso en línea: | http://hdl.handle.net/11336/150245 |
| Access Level: | acceso abierto |
| Palabra clave: | DISCRETE FOURIER TRANSFORM DISTRIBUTED QUERY RANKING ALGORITHM QUERY SCHEDULING FOR MULTI-CORE PROCESSORS https://purl.org/becyt/ford/1.2 https://purl.org/becyt/ford/1 |
| Sumario: | Web search engines are built from components capable of processing large amounts of user queries per second in a distributed way. Among them, the index service computes the topk documents that best match each incoming query by means of a document ranking operation. To achieve high performance, dynamic pruning techniques such as the WAND and BM-WAND algorithms are used to avoid fully processing all of the documents related to a query during the ranking operation. Additionally, the index service distributes the ranking operations among clusters of processors wherein in each processor multi-threading is applied to speed up query solution. In this scenario, a query running time prediction algorithm has practical applications in the efficient assignment of processors and threads to incoming queries. We propose a prediction algorithm for the WAND and BM-WAND algorithms. We experimentally show that our proposal is able to achieve accurate prediction results while significantly reducing execution time and memory consumption as compared against an alternative prediction algorithm. Our proposal applies the discrete Fourier transform (DFT) to represent key features affecting query running time whereas the resulting vectors are used to train a feed-forward neural network with back-propagation. |
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