research-article

Automatic virtual machine configuration for database workloads

Authors:

Ahmed A. Soror,

Umar Farooq Minhas,

Ashraf Aboulnaga,

Peter Kokosielis,

Sunil KamathAuthors Info & Claims

SIGMOD '08: Proceedings of the 2008 ACM SIGMOD international conference on Management of data

Pages 953 - 966

https://doi.org/10.1145/1376616.1376711

Published: 09 June 2008 Publication History

Abstract

Virtual machine monitors are becoming popular tools for the deployment of database management systems and other enterprise software applications. In this paper, we consider a common resource consolidation scenario, in which several database management system instances, each running in a virtual machine, are sharing a common pool of physical computing resources. We address the problem of optimizing the performance of these database management systems by controlling the configurations of the virtual machines in which they run. These virtual machine configurations determine how the shared physical resources will be allocated to the different database instances. We introduce a virtualization design advisor that uses information about the anticipated workloads of each of the database systems to recommend workload-specific configurations offine. Furthermore, runtime information collected after the deployment of the recommended configurations can be used to refine the recommendation. To estimate the effect of a particular resource allocation on workload performance, we use the query optimizer in a new what-if mode. We have implemented our approach using both PostgreSQL and DB2, and we have experimentally evaluated its effectiveness using DSS and OLTP workloads.

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Cited By

Pavlenko ACahoon JZhu YKroth BNelson MCarter ALiao DWright TCamacho-Rodríguez JSaur KBarcelo PSanchez-Pi NMeliou ASudarshan S(2024)Vertically Autoscaling Monolithic Applications with CaaSPER: Scalable Container-as-a-Service Performance Enhanced Resizing Algorithm for the CloudCompanion of the 2024 International Conference on Management of Data10.1145/3626246.3653378(241-254)Online publication date: 9-Jun-2024
https://dl.acm.org/doi/10.1145/3626246.3653378
Gunasekaran KTiwari KAcharya R(2023)Utilizing deep learning for automated tuning of database management systems2023 International Conference on Communications, Computing and Artificial Intelligence (CCCAI)10.1109/CCCAI59026.2023.00022(75-81)Online publication date: Jun-2023
https://doi.org/10.1109/CCCAI59026.2023.00022
Chen YHoque MXu PLu JTarkoma S(2023)SimCost: cost-effective resource provision prediction and recommendation for spark workloadsDistributed and Parallel Databases10.1007/s10619-023-07436-y42:1(73-102)Online publication date: 22-Jun-2023
https://doi.org/10.1007/s10619-023-07436-y
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Index Terms

Automatic virtual machine configuration for database workloads
1. Information systems
  1. Data management systems
    1. Database design and models
      1. Physical data models

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Published In

cover image ACM Conferences

SIGMOD '08: Proceedings of the 2008 ACM SIGMOD international conference on Management of data

June 2008

1396 pages

ISBN:9781605581026

DOI:10.1145/1376616

General Chairs:
Laks V. S. Lakshmanan
University of British Columbia, Canada
,
Raymond T. Ng
University of British Columbia, Canada
,
Dennis Shasha
New York University, USA

Copyright © 2008 ACM.

Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

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Published: 09 June 2008

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SIGMOD/PODS '08

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SIGMOD/PODS '08: SIGMOD/PODS '08 - International Conference on Management of Data

June 9 - 12, 2008

Vancouver, Canada

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Overall Acceptance Rate 785 of 4,003 submissions, 20%

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Cited By

Pavlenko ACahoon JZhu YKroth BNelson MCarter ALiao DWright TCamacho-Rodríguez JSaur KBarcelo PSanchez-Pi NMeliou ASudarshan S(2024)Vertically Autoscaling Monolithic Applications with CaaSPER: Scalable Container-as-a-Service Performance Enhanced Resizing Algorithm for the CloudCompanion of the 2024 International Conference on Management of Data10.1145/3626246.3653378(241-254)Online publication date: 9-Jun-2024
https://dl.acm.org/doi/10.1145/3626246.3653378
Gunasekaran KTiwari KAcharya R(2023)Utilizing deep learning for automated tuning of database management systems2023 International Conference on Communications, Computing and Artificial Intelligence (CCCAI)10.1109/CCCAI59026.2023.00022(75-81)Online publication date: Jun-2023
https://doi.org/10.1109/CCCAI59026.2023.00022
Chen YHoque MXu PLu JTarkoma S(2023)SimCost: cost-effective resource provision prediction and recommendation for spark workloadsDistributed and Parallel Databases10.1007/s10619-023-07436-y42:1(73-102)Online publication date: 22-Jun-2023
https://doi.org/10.1007/s10619-023-07436-y
Bajaj S(2021)Current Drift in Energy Efficiency Cloud ComputingResearch Anthology on Architectures, Frameworks, and Integration Strategies for Distributed and Cloud Computing10.4018/978-1-7998-5339-8.ch057(1198-1214)Online publication date: 2021
https://doi.org/10.4018/978-1-7998-5339-8.ch057
Chen YLu JChen CHoque MTarkoma SZhu WTao DCheng XCui PRundensteiner ECarmel DHe QXu Yu J(2019)Cost-effective Resource Provisioning for Spark WorkloadsProceedings of the 28th ACM International Conference on Information and Knowledge Management10.1145/3357384.3358090(2477-2480)Online publication date: 3-Nov-2019
https://dl.acm.org/doi/10.1145/3357384.3358090
Chen YGoetsch PHoque MLu JTarkoma S(2019)d-Simplexed: Adaptive Delaunay Triangulation for Performance Modeling and Prediction on Big Data AnalyticsIEEE Transactions on Big Data10.1109/TBDATA.2019.2948338(1-1)Online publication date: 2019
https://doi.org/10.1109/TBDATA.2019.2948338
Bajaj S(2018)Current Drift in Energy Efficiency Cloud ComputingCritical Research on Scalability and Security Issues in Virtual Cloud Environments10.4018/978-1-5225-3029-9.ch014(283-303)Online publication date: 2018
https://doi.org/10.4018/978-1-5225-3029-9.ch014
Sousa FMoreira LCosta Filho JMachado J(2018)Predictive elastic replication for multi‐tenant databases in the cloudConcurrency and Computation: Practice and Experience10.1002/cpe.443730:16Online publication date: Feb-2018
https://doi.org/10.1002/cpe.4437
Maghawry EIsmail RBadr NTolba M(2017)Workload Management Systems for the Cloud EnvironmentHandbook of Research on Machine Learning Innovations and Trends10.4018/978-1-5225-2229-4.ch005(94-113)Online publication date: 2017
https://doi.org/10.4018/978-1-5225-2229-4.ch005
Van Aken DPavlo AGordon GZhang BChirkova RYang JSuciu D(2017)Automatic Database Management System Tuning Through Large-scale Machine LearningProceedings of the 2017 ACM International Conference on Management of Data10.1145/3035918.3064029(1009-1024)Online publication date: 9-May-2017
https://dl.acm.org/doi/10.1145/3035918.3064029
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