STeP: Scalable tenant placement for managing database-as-a-service deployments

Rebecca Taft, Willis Lang, Jennie M Rogers, Aaron J. Elmore, Michael Stonebraker, David De Witt

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

6 Citations (Scopus)

Abstract

Public cloud providers with Database-as-a-Service offerings must efficiently allocate computing resources to each of their customers. An effective assignment of tenants both reduces the number of physical servers in use and meets customer expectations at a price point that is competitive in the cloud market. For public cloud vendors like Microsoft and Amazon, this means packing millions of users' databases onto hundreds or thousands of servers. This paper studies tenant placement by examining a publicly released dataset of anonymized customer resource usage statistics from Microsoft's Azure SQL Database production system over a three-month period. We implemented the STeP framework to ingest and analyze this large dataset. STeP allowed us to use this production dataset to evaluate several new algorithms for packing database tenants onto servers. These techniques produce highly efficient packings by collocating tenants with compatible resource usage patterns. The evaluation shows that under a production-sourced customer workload, these techniques are robust to variations in the number of nodes, keeping performance objective violations to a minimum even for high-density tenant packings. In comparison to the algorithm used in production at the time of data collection, our algorithms produce up to 90% fewer performance objective violations and save up to 32% of total operational costs for the cloud provider.

Original languageEnglish (US)
Title of host publicationProceedings of the 7th ACM Symposium on Cloud Computing, SoCC 2016
PublisherAssociation for Computing Machinery, Inc
Pages388-400
Number of pages13
ISBN (Electronic)9781450345255
DOIs
StatePublished - Oct 5 2016
Event7th ACM Symposium on Cloud Computing, SoCC 2016 - Santa Clara, United States
Duration: Oct 5 2016Oct 7 2016

Other

Other7th ACM Symposium on Cloud Computing, SoCC 2016
CountryUnited States
CitySanta Clara
Period10/5/1610/7/16

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Keywords

  • Cloud database
  • Database-as-a-Service
  • Multitenancy

ASJC Scopus subject areas

  • Computer Science Applications
  • Computational Theory and Mathematics

Cite this

Taft, R., Lang, W., Rogers, J. M., Elmore, A. J., Stonebraker, M., & De Witt, D. (2016). STeP: Scalable tenant placement for managing database-as-a-service deployments. In Proceedings of the 7th ACM Symposium on Cloud Computing, SoCC 2016 (pp. 388-400). Association for Computing Machinery, Inc. https://doi.org/10.1145/2987550.2987575
Taft, Rebecca ; Lang, Willis ; Rogers, Jennie M ; Elmore, Aaron J. ; Stonebraker, Michael ; De Witt, David. / STeP : Scalable tenant placement for managing database-as-a-service deployments. Proceedings of the 7th ACM Symposium on Cloud Computing, SoCC 2016. Association for Computing Machinery, Inc, 2016. pp. 388-400
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Taft, R, Lang, W, Rogers, JM, Elmore, AJ, Stonebraker, M & De Witt, D 2016, STeP: Scalable tenant placement for managing database-as-a-service deployments. in Proceedings of the 7th ACM Symposium on Cloud Computing, SoCC 2016. Association for Computing Machinery, Inc, pp. 388-400, 7th ACM Symposium on Cloud Computing, SoCC 2016, Santa Clara, United States, 10/5/16. https://doi.org/10.1145/2987550.2987575

STeP : Scalable tenant placement for managing database-as-a-service deployments. / Taft, Rebecca; Lang, Willis; Rogers, Jennie M; Elmore, Aaron J.; Stonebraker, Michael; De Witt, David.

Proceedings of the 7th ACM Symposium on Cloud Computing, SoCC 2016. Association for Computing Machinery, Inc, 2016. p. 388-400.

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

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Taft R, Lang W, Rogers JM, Elmore AJ, Stonebraker M, De Witt D. STeP: Scalable tenant placement for managing database-as-a-service deployments. In Proceedings of the 7th ACM Symposium on Cloud Computing, SoCC 2016. Association for Computing Machinery, Inc. 2016. p. 388-400 https://doi.org/10.1145/2987550.2987575