Cloud Query Processing with Reinforcement Learning-Based Multi-objective Re-optimization

Chenxiao Wang, Le Gruenwald, Laurent d’Orazio, Eleazar Leal

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

Query processing on cloud database systems is a challenging problem due to the dynamic cloud environment. The configuration and utilization of the distributed hardware used to process queries change continuously. A query optimizer aims to generate query execution plans (QEPs) that are optimal meet user requirements. In order to achieve such QEPs under dynamic environments, performing query re-optimizations during query execution has been proposed in the literature. In cloud database systems, besides query execution time, users also consider the monetary cost to be paid to the cloud provider for executing queries. Thus, such query re-optimizations are multi-objective optimizations which take both time and monetary costs into consideration. However, traditional re-optimization requires accurate cost estimations, and obtaining these estimations adds overhead to the system, and thus causes negative impacts on query performance. To fill this gap, in this paper, we introduce ReOptRL, a novel query processing algorithm based on deep reinforcement learning. It bootstraps a QEP generated by an existing query optimizer and dynamically changes the QEP during the query execution. It also keeps learning from incoming queries to build a more accurate optimization model. In this algorithm, the QEP of a query is adjusted based on the recent performance of the same query so that the algorithm does not rely on cost estimations. Our experiments show that the proposed algorithm performs better than existing query optimization algorithms in terms of query execution time and query execution monetary costs.

Original languageEnglish (US)
Title of host publicationModel and Data Engineering - 10th International Conference, MEDI 2021, Proceedings
EditorsChristian Attiogbé, Sadok Ben Yahia
PublisherSpringer Science and Business Media Deutschland GmbH
Pages141-155
Number of pages15
ISBN (Print)9783030784270
DOIs
StatePublished - 2021
Event10th International Conference on Model and Data Engineering, MEDI 2021 - Virtual, Online
Duration: Jun 21 2021Jun 23 2021

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume12732 LNCS
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349

Conference

Conference10th International Conference on Model and Data Engineering, MEDI 2021
CityVirtual, Online
Period6/21/216/23/21

Bibliographical note

Publisher Copyright:
© 2021, Springer Nature Switzerland AG.

Keywords

  • Cloud databases
  • Query optimization
  • Query re-optimization
  • Reinforcement learning

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