research-article

Strategy-Proof Mechanism for Provisioning and Allocation Virtual Machines in Heterogeneous Clouds

Authors:

Xuejie ZhangAuthors Info & Claims

IEEE Transactions on Parallel and Distributed Systems, Volume 29, Issue 7

Pages 1650 - 1663

https://doi.org/10.1109/TPDS.2017.2785815

Published: 01 July 2018 Publication History

Abstract

In this paper, we address the problem of heterogeneous physical machines resource management (HPMRM); that is, providing and allocating multiple virtual machine (VM) instances from heterogeneous physical machines to maximize social welfare. Although existing allocation mechanisms allocate VMs to users through the single-mapping mechanism, such allocations cannot guarantee maximum social welfare or efficient utilization of multiple types of resources for cloud providers. Thus, we consider the multi-mapping mechanism, which permits mapping VMs allocated to one user to physical machines for VM provisioning and allocation. This can result in improved social welfare and lead to less resource fragmentation. We formulate the HPMRM problem in an auction-based setting, and design optimal and approximate mechanisms to solve it. In addition, we show that our proposed mechanism is strategy-proof; that is, our proposed mechanism drives the system into an equilibrium where no users have incentives to maximize their own profit by untruthfully reporting their requests. Furthermore, we analyze the approximation ratio of our proposed approximation algorithm. We also perform experiments to investigate the performance of our proposed approximation mechanism compared to the optimal mechanism. Experimental results demonstrate that our proposed approximation mechanism can obtain near optimal solutions and significantly improve allocation efficiency, while generating greater social welfare.

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Cited By

Zhang JWang ZVasilakos ALi W(2024)A resource competition-based truthful mechanism for IoV edge computing resource allocation with a lowest revenue limitJournal of Cloud Computing: Advances, Systems and Applications10.1186/s13677-023-00572-x13:1Online publication date: 10-Jan-2024
https://dl.acm.org/doi/10.1186/s13677-023-00572-x
Zhang JZhang YWu HLi W(2024)An Ordered Submodularity-Based Budget-Feasible Mechanism for Opportunistic Mobile Crowdsensing Task Allocation and PricingIEEE Transactions on Mobile Computing10.1109/TMC.2022.323251323:2(1278-1294)Online publication date: 1-Feb-2024
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https://dl.acm.org/doi/10.1145/3546000.3546027
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Strategy-Proof Mechanism for Provisioning and Allocation Virtual Machines in Heterogeneous Clouds

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cover image IEEE Transactions on Parallel and Distributed Systems

IEEE Transactions on Parallel and Distributed Systems Volume 29, Issue 7

July 2018

236 pages

ISSN:1045-9219

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1045-9219 © 2017 IEEE. Personal use is permitted, but republication/redistribution requires IEEE permission. See http://www.ieee.org/publications standards/publications/rights/index.html for more information.

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Published: 01 July 2018

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Cited By

Zhang JWang ZVasilakos ALi W(2024)A resource competition-based truthful mechanism for IoV edge computing resource allocation with a lowest revenue limitJournal of Cloud Computing: Advances, Systems and Applications10.1186/s13677-023-00572-x13:1Online publication date: 10-Jan-2024
https://dl.acm.org/doi/10.1186/s13677-023-00572-x
Zhang JZhang YWu HLi W(2024)An Ordered Submodularity-Based Budget-Feasible Mechanism for Opportunistic Mobile Crowdsensing Task Allocation and PricingIEEE Transactions on Mobile Computing10.1109/TMC.2022.323251323:2(1278-1294)Online publication date: 1-Feb-2024
https://dl.acm.org/doi/10.1109/TMC.2022.3232513
Zhang QLi WSu QZhang X(2022)A Local-Ratio-Based Power Control Approach for Capacitated Access Points in Mobile Edge ComputingProceedings of the 6th International Conference on High Performance Compilation, Computing and Communications10.1145/3546000.3546027(175-182)Online publication date: 23-Jun-2022
https://dl.acm.org/doi/10.1145/3546000.3546027
Chen ZHu JMin GLuo CEl-Ghazawi T(2022)Adaptive and Efficient Resource Allocation in Cloud Datacenters Using Actor-Critic Deep Reinforcement LearningIEEE Transactions on Parallel and Distributed Systems10.1109/TPDS.2021.313242233:8(1911-1923)Online publication date: 1-Aug-2022
https://dl.acm.org/doi/10.1109/TPDS.2021.3132422

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