research-article

Efficient resource provisioning in compute clouds via VM multiplexing

Authors:

Jeffrey Kephart,

Dimitrios PendarakisAuthors Info & Claims

ICAC '10: Proceedings of the 7th international conference on Autonomic computing

Pages 11 - 20

https://doi.org/10.1145/1809049.1809052

Published: 07 June 2010 Publication History

Abstract

Resource provisioning in compute clouds often require an estimate of the capacity needs of Virtual Machines (VMs). The estimated VM size is the basis for allocating resources commensurate with workload demand. In contrast to the traditional practice of estimating the VM sizes individually, we propose a joint-VM sizing approach in which multiple VMs are consolidated and provisioned, based on an estimate of their aggregate capacity needs. This new approach exploits statistical multiplexing among the workload patterns of multiple VMs, i.e., the peaks and valleys in one workload pattern do not necessarily coincide with the others. Thus, the unused resources of a low utilized VM can be directed to the other co-located VMs with high utilization. Compared to individual VM based provisioning, joint-VM sizing and provisioning may lead to much higher resource utilization. This paper presents three design modules to enable the concept in practice. Specifically, a performance constraint describing the capacity need of a VM for achieving a certain level of application performance; an algorithm for estimating the size of jointly provisioning VMs; a VM selection method that seeks to find good VM combinations for being provisioned together. We showcase that the proposed three modules can be seamlessly plugged into existing applications such as resource provisioning, and providing resource guarantees for VMs. The proposed algorithms and applications are evaluated by monitoring data collected from about 16 thousand VMs in commercial data centers. These evaluations reveal more than 45% improvements in terms of the overall resource utilization.

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Cited By

Tütüncüoğlu FBen-Ameur ADán GAraldo AChahed T(2024)Dynamic Time-of-Use Pricing for Serverless Edge Computing with Generalized Hidden Parameter Markov Decision Processes2024 IEEE 44th International Conference on Distributed Computing Systems (ICDCS)10.1109/ICDCS60910.2024.00068(668-679)Online publication date: 23-Jul-2024
https://doi.org/10.1109/ICDCS60910.2024.00068
Li WZhou KShi JChen XLiu YWang LGu J(2024)Understanding the IO Performance Gap Between OS-Level and VM-Level Containers in High-Density Deployment2024 IEEE 44th International Conference on Distributed Computing Systems (ICDCS)10.1109/ICDCS60910.2024.00048(438-449)Online publication date: 23-Jul-2024
https://doi.org/10.1109/ICDCS60910.2024.00048
Sharma ASingh JGulzar YGupta DKumar M(2024)Sustainable energy efficient workflow classification and scheduling in geo distributed cloud datacenterDiscover Sustainability10.1007/s43621-024-00308-05:1Online publication date: 25-Jun-2024
https://doi.org/10.1007/s43621-024-00308-0
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Index Terms

Efficient resource provisioning in compute clouds via VM multiplexing
1. Computing methodologies
  1. Modeling and simulation
    1. Model development and analysis
      1. Modeling methodologies

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Published In

cover image ACM Conferences

ICAC '10: Proceedings of the 7th international conference on Autonomic computing

June 2010

246 pages

ISBN:9781450300742

DOI:10.1145/1809049

General Chair:
Manish Parashar
Rutgers University, USA
,
Program Chairs:
Renato Figueiredo
University of Florida, USA
,
Emre Kıcıman
Microsoft Research, USA

Copyright © 2010 ACM.

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Published: 07 June 2010

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ICAC '10: 7th International Conference on Autonomic Computing

June 7 - 11, 2010

DC, Washington, USA

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Cited By

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https://doi.org/10.1109/ICDCS60910.2024.00068
Li WZhou KShi JChen XLiu YWang LGu J(2024)Understanding the IO Performance Gap Between OS-Level and VM-Level Containers in High-Density Deployment2024 IEEE 44th International Conference on Distributed Computing Systems (ICDCS)10.1109/ICDCS60910.2024.00048(438-449)Online publication date: 23-Jul-2024
https://doi.org/10.1109/ICDCS60910.2024.00048
Sharma ASingh JGulzar YGupta DKumar M(2024)Sustainable energy efficient workflow classification and scheduling in geo distributed cloud datacenterDiscover Sustainability10.1007/s43621-024-00308-05:1Online publication date: 25-Jun-2024
https://doi.org/10.1007/s43621-024-00308-0
Patra MSahoo BTuruk AMisra S(2023)Task grouping and optimized deep learning based VM sizing for hosting containers as a serviceJournal of Cloud Computing10.1186/s13677-023-00441-712:1Online publication date: 25-Apr-2023
https://doi.org/10.1186/s13677-023-00441-7
Kang SHwang JKim Y(2023)DAQS: Dynamic and Accurate QoS for SR-IOV2023 14th International Conference on Information and Communication Technology Convergence (ICTC)10.1109/ICTC58733.2023.10392450(1440-1442)Online publication date: 11-Oct-2023
https://doi.org/10.1109/ICTC58733.2023.10392450
Thiruvenkadam TMuthusamy AVijayakumar M(2023)Resource Request Handling Mechanisms for Effective VM Placements in Cloud EnvironmentExpert Clouds and Applications10.1007/978-981-99-1745-7_13(181-194)Online publication date: 2-Jul-2023
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Kaur GChawla S(2023)Dynamic Resource Allocation Framework in Cloud ComputingSoft Computing and Signal Processing10.1007/978-981-19-8669-7_27(297-306)Online publication date: 27-Jun-2023
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Kocharyan AEkane BTeabe BTran GAstsatryan HHagimont D(2022)A Remote Memory Sharing System for Virtualized Computing InfrastructuresIEEE Transactions on Cloud Computing10.1109/TCC.2020.301808910:3(1532-1542)Online publication date: 1-Jul-2022
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Guo CLi YYan YChen WBose SShen G(2022)Efficiently Consolidating Virtual Data Centers for Time-Varying Resource DemandsIEEE Transactions on Cloud Computing10.1109/TCC.2020.299740310:3(1751-1764)Online publication date: 1-Jul-2022
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