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CoSpot: a cooperative VM allocation framework for increased revenue from spot instances

Authors:

Ivan Beschastnikh,

Alan J. HuAuthors Info & Claims

SoCC '22: Proceedings of the 13th Symposium on Cloud Computing

Pages 540 - 556

https://doi.org/10.1145/3542929.3563499

Published: 07 November 2022 Publication History

Abstract

Most large cloud operators offer a lower-priced, lower-priority alternative to regular (on-demand or reserved) virtual machines, commonly referred to as spot instances. Spot instances are opportunistically allocated to servers in order to utilize any residual cloud capacity, but are evicted whenever regular virtual machines need to use that capacity. This paper proposes CoSpot, a lightweight framework for cooperative allocation of regular virtual machines and spot instances, which allows for easy integration of arbitrary virtual machine and spot allocators. In our experiments, employing the framework achieves up to 245% improvement (average 34% improvement) in spot revenue, with no loss in virtual machine revenue, compared to the baseline VM and spot allocation without using our framework. We also derive and release a reusable workload with both virtual machines and spot instances, based on data previously shared by Microsoft Azure.

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Cited By

Wu ZChiang WMao ZYang ZFriedman EShenker SStoica IVanbever LZhang I(2024)Can't be lateProceedings of the 21st USENIX Symposium on Networked Systems Design and Implementation10.5555/3691825.3691836(185-203)Online publication date: 16-Apr-2024
https://dl.acm.org/doi/10.5555/3691825.3691836
Bhasi VSharma AJain RGunasekaran JPattnaik AKandemir MDas CSchiavoni VEdinger JCao JJin Z(2024)Towards SLO-Compliant and Cost-Effective Serverless Computing on Emerging GPU ArchitecturesProceedings of the 25th International Middleware Conference10.1145/3652892.3700760(211-224)Online publication date: 2-Dec-2024
https://dl.acm.org/doi/10.1145/3652892.3700760
Shang RXu FBai ZChen LZhou ZLiu F(2023)spotDNN: Provisioning Spot Instances for Predictable Distributed DNN Training in the Cloud2023 IEEE/ACM 31st International Symposium on Quality of Service (IWQoS)10.1109/IWQoS57198.2023.10188717(1-10)Online publication date: 19-Jun-2023
https://doi.org/10.1109/IWQoS57198.2023.10188717

Index Terms

CoSpot: a cooperative VM allocation framework for increased revenue from spot instances
1. Computer systems organization
  1. Architectures
    1. Distributed architectures
      1. Cloud computing
2. Social and professional topics
  1. Professional topics
    1. Management of computing and information systems
      1. Implementation management
        Pricing and resource allocation

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Published In

cover image ACM Conferences

SoCC '22: Proceedings of the 13th Symposium on Cloud Computing

November 2022

574 pages

ISBN:9781450394147

DOI:10.1145/3542929

General Chair:
Ada Gavrilovska
Georgia Institute of Technology
,
Program Chairs:
Deniz Altınbüken
Google Research
,
Carsten Binnig
TU Darmstadt

Copyright © 2022 Owner/Author.

This work is licensed under a Creative Commons Attribution International 4.0 License.

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Published: 07 November 2022

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November 7 - 11, 2022

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Cited By

Wu ZChiang WMao ZYang ZFriedman EShenker SStoica IVanbever LZhang I(2024)Can't be lateProceedings of the 21st USENIX Symposium on Networked Systems Design and Implementation10.5555/3691825.3691836(185-203)Online publication date: 16-Apr-2024
https://dl.acm.org/doi/10.5555/3691825.3691836
Bhasi VSharma AJain RGunasekaran JPattnaik AKandemir MDas CSchiavoni VEdinger JCao JJin Z(2024)Towards SLO-Compliant and Cost-Effective Serverless Computing on Emerging GPU ArchitecturesProceedings of the 25th International Middleware Conference10.1145/3652892.3700760(211-224)Online publication date: 2-Dec-2024
https://dl.acm.org/doi/10.1145/3652892.3700760
Shang RXu FBai ZChen LZhou ZLiu F(2023)spotDNN: Provisioning Spot Instances for Predictable Distributed DNN Training in the Cloud2023 IEEE/ACM 31st International Symposium on Quality of Service (IWQoS)10.1109/IWQoS57198.2023.10188717(1-10)Online publication date: 19-Jun-2023
https://doi.org/10.1109/IWQoS57198.2023.10188717

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