research-article

PMAX: tenant placement in multitenant databases for profit maximization

Authors:

Hakan Hacıgümüş,

Wang-Pin HsiungAuthors Info & Claims

EDBT '13: Proceedings of the 16th International Conference on Extending Database Technology

Pages 442 - 453

https://doi.org/10.1145/2452376.2452428

Published: 18 March 2013 Publication History

Abstract

There has been a great interest in exploiting the cloud as a platform for database as a service. As with other cloud-based services, database services may enjoy cost efficiency through consolidation: hosting multiple databases within a single physical server. Aggressive consolidation, however, may hurt the service quality, leading to SLA violation penalty, which in turn reduces the total business profit, called SLA profit. In this paper, we consider the problem of tenant placement in the cloud for SLA profit maximization, which, as will be shown in the paper, is strongly NP-hard. We propose SLA profit-aware solutions for database tenant placement based on our model for expected penalty computation for multitenant servers. Specifically, we present two approximation algorithms, which have constant approximation ratios, and we further discuss improving the quality of tenant placement using a dynamic programming algorithm. Extensive experiments based on TPC-W workload verified the performance of the proposed approaches.

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Cited By

Liu RElmore AFranklin MKrishnan S(2024)Riveter: Adaptive Query Suspension and Resumption Framework for Cloud Native Databases2024 IEEE 40th International Conference on Data Engineering (ICDE)10.1109/ICDE60146.2024.00304(3975-3988)Online publication date: 13-May-2024
https://doi.org/10.1109/ICDE60146.2024.00304
Xiao WYang CWang JZhu XBao WFeng XXie YCao WYu FLiu L(2023)YISHAN: Managing Large-scale Cloud Database Instances via Machine LearningIEEE Transactions on Services Computing10.1109/TSC.2021.313124916:1(724-738)Online publication date: 1-Jan-2023
https://doi.org/10.1109/TSC.2021.3131249
Zhang YMing ZPeng XLiu JZhang L(2023)LBFF: Load-Balancing First Fit Algorithm for Tenant Placement ProblemICC 2023 - IEEE International Conference on Communications10.1109/ICC45041.2023.10279638(6261-6267)Online publication date: 28-May-2023
https://doi.org/10.1109/ICC45041.2023.10279638
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Index Terms

PMAX: tenant placement in multitenant databases for profit maximization
1. Computing methodologies
  1. Modeling and simulation
    1. Model development and analysis
      1. Modeling methodologies

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cover image ACM Other conferences

EDBT '13: Proceedings of the 16th International Conference on Extending Database Technology

March 2013

793 pages

ISBN:9781450315975

DOI:10.1145/2452376

General Chair:
Giovanna Guerrini
Università di Genova, Italy
,
Program Chair:
Norman W. Paton
University of Manchester, UK

Copyright © 2013 ACM.

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Association for Computing Machinery

New York, NY, United States

Publication History

Published: 18 March 2013

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EDBT/ICDT '13

EDBT/ICDT '13: Joint 2013 EDBT/ICDT Conferences

March 18 - 22, 2013

Genoa, Italy

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Overall Acceptance Rate 7 of 10 submissions, 70%

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Cited By

Liu RElmore AFranklin MKrishnan S(2024)Riveter: Adaptive Query Suspension and Resumption Framework for Cloud Native Databases2024 IEEE 40th International Conference on Data Engineering (ICDE)10.1109/ICDE60146.2024.00304(3975-3988)Online publication date: 13-May-2024
https://doi.org/10.1109/ICDE60146.2024.00304
Xiao WYang CWang JZhu XBao WFeng XXie YCao WYu FLiu L(2023)YISHAN: Managing Large-scale Cloud Database Instances via Machine LearningIEEE Transactions on Services Computing10.1109/TSC.2021.313124916:1(724-738)Online publication date: 1-Jan-2023
https://doi.org/10.1109/TSC.2021.3131249
Zhang YMing ZPeng XLiu JZhang L(2023)LBFF: Load-Balancing First Fit Algorithm for Tenant Placement ProblemICC 2023 - IEEE International Conference on Communications10.1109/ICC45041.2023.10279638(6261-6267)Online publication date: 28-May-2023
https://doi.org/10.1109/ICC45041.2023.10279638
Abdel Raouf AAbo‐alian ABadr N(2021) A predictive replication for multi‐tenant databases using deep learning Concurrency and Computation: Practice and Experience10.1002/cpe.622633:13Online publication date: 12-Feb-2021
https://doi.org/10.1002/cpe.6226
Kantere V(2020)Processing Big Data Across InfrastructuresBig Data – BigData 202010.1007/978-3-030-59612-5_4(38-51)Online publication date: 18-Sep-2020
https://doi.org/10.1007/978-3-030-59612-5_4
Das JDasgupta AGhosh SBuyya R(2018)A Learning Technique for VM Allocation to Resolve Geospatial QueriesRecent Findings in Intelligent Computing Techniques10.1007/978-981-10-8639-7_61(577-584)Online publication date: 4-Nov-2018
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Rosinosky GLabba CFerme VYoucef SCharoy FPautasso C(2018)Evaluating Multi-tenant Live Migrations Effects on PerformanceOn the Move to Meaningful Internet Systems. OTM 2018 Conferences10.1007/978-3-030-02610-3_4(61-77)Online publication date: 18-Oct-2018
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Molka KCasale G(2017)Energy-efficient resource allocation and provisioning for in-memory database clusters2017 IFIP/IEEE Symposium on Integrated Network and Service Management (IM)10.23919/INM.2017.7987260(19-27)Online publication date: May-2017
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Ortiz JCheung AElmore A(2017)PerfEnforce OverviewProceedings of the 2017 ACM International Conference on Management of Data10.1145/3055167.3055175(31-33)Online publication date: 14-May-2017
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Zhang WShi YZheng YLiu LCui L(2017)Resource and performance prediction at high utilization for N-Tier cloud-based service systemsProceedings of the Australasian Computer Science Week Multiconference10.1145/3014812.3014857(1-9)Online publication date: 30-Jan-2017
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