research-article

Adaptive live migration of virtual machines under limited network bandwidth

Authors:

Guangrong Xiao,

Jianguo YaoAuthors Info & Claims

VEE 2021: Proceedings of the 17th ACM SIGPLAN/SIGOPS International Conference on Virtual Execution Environments

Pages 98 - 110

https://doi.org/10.1145/3453933.3454017

Published: 07 April 2021 Publication History

Abstract

Live migration is a crucial feature in existing virtualization platforms. Since memory is dirtied rapidly during the execution of a virtual machine (VM), boosting memory migration speed becomes a significant factor in guaranteeing a high-level success ratio and efficiency. However, the statically-configured migration strategy cannot cope with various workloads running in VMs, resulting in frequently aborted migration processes and low success ratio. This paper proposed a one-for-all migration architecture called Adaptive Live Migration (AdaMig) to address these issues. This QEMU-based solution dynamically switches migration methods and tunes related parameters by monitoring the run-time statistics from the migration process and the physical host. Once AdaMig detects the tendency that migration cannot converge, it will switch to another migration method to synchronize remaining dirty pages. During the whole process, AdaMig also dynamically tunes migration parameters according to current resources available in the physical host and migration efficiency. Experimental results reflect that AdaMig improves the success ratio from 26.7% to 93.3% over various workloads, and migration time is reduced by up to 45.5% in comparison with the original solution in QEMU.

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Cited By

Li YMa XFu ZZhou AXu MWang S(2025)Rethinking Cost-Efficient VM Scheduling on Public Edge Platforms: A Service Provider’s PerspectiveIEEE Transactions on Mobile Computing10.1109/TMC.2024.348808224:3(1846-1858)Online publication date: Mar-2025
https://doi.org/10.1109/TMC.2024.3488082
Chen YXu SYang HZhou RGuo DZhou Q(2024)Live Migration of Virtual Machines Based on Dirty Page SimilarityIEEE Transactions on Cloud Computing10.1109/TCC.2024.337949412:2(563-579)Online publication date: Apr-2024
https://doi.org/10.1109/TCC.2024.3379494
Fernando DTerner JYang PGopalan K(2023)V-Recover: Virtual Machine Recovery When Live Migration FailsIEEE Transactions on Cloud Computing10.1109/TCC.2023.328246611:3(3289-3300)Online publication date: 1-Jul-2023
https://doi.org/10.1109/TCC.2023.3282466
Show More Cited By

Index Terms

Adaptive live migration of virtual machines under limited network bandwidth
1. Software and its engineering
  1. Software organization and properties
    1. Contextual software domains
      1. Operating systems
        Process management
      2. Software infrastructure
        Virtual machines

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Published In

cover image ACM Conferences

VEE 2021: Proceedings of the 17th ACM SIGPLAN/SIGOPS International Conference on Virtual Execution Environments

April 2021

200 pages

ISBN:9781450383943

DOI:10.1145/3453933

General Chair:
Ben L. Titzer
Google, Germany
,
Program Chairs:
Harry Xu
University of California at Los Angeles, USA
,
Irene Zhang
Microsoft Research, USA

Copyright © 2021 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: 07 April 2021

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VEE '21: 17th ACM SIGPLAN/SIGOPS International Conference on Virtual Execution Environments

April 16, 2021

Virtual, USA

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Cited By

Li YMa XFu ZZhou AXu MWang S(2025)Rethinking Cost-Efficient VM Scheduling on Public Edge Platforms: A Service Provider’s PerspectiveIEEE Transactions on Mobile Computing10.1109/TMC.2024.348808224:3(1846-1858)Online publication date: Mar-2025
https://doi.org/10.1109/TMC.2024.3488082
Chen YXu SYang HZhou RGuo DZhou Q(2024)Live Migration of Virtual Machines Based on Dirty Page SimilarityIEEE Transactions on Cloud Computing10.1109/TCC.2024.337949412:2(563-579)Online publication date: Apr-2024
https://doi.org/10.1109/TCC.2024.3379494
Fernando DTerner JYang PGopalan K(2023)V-Recover: Virtual Machine Recovery When Live Migration FailsIEEE Transactions on Cloud Computing10.1109/TCC.2023.328246611:3(3289-3300)Online publication date: 1-Jul-2023
https://doi.org/10.1109/TCC.2023.3282466
Asanuma KYamada H(2023)DBMS-Assisted Live Migration of Virtual MachinesIEEE Transactions on Computers10.1109/TC.2023.332994373:2(380-393)Online publication date: 7-Nov-2023
https://dl.acm.org/doi/10.1109/TC.2023.3329943
Deng PGong XQue X(2023)A bandwidth-aware service migration method in LEO satellite edge computing networkComputer Communications10.1016/j.comcom.2023.01.007200:C(104-112)Online publication date: 15-Feb-2023
https://dl.acm.org/doi/10.1016/j.comcom.2023.01.007
Haris RKhan KNhlabatsi ABarhamgi M(2023)A machine learning-based optimization approach for pre-copy live virtual machine migrationCluster Computing10.1007/s10586-023-04001-127:2(1293-1312)Online publication date: 9-May-2023
https://dl.acm.org/doi/10.1007/s10586-023-04001-1
Haris RKhan KNhlabatsi A(undefined)A Machine Learning-Based Optimization Approach for Pre-Copy Live Virtual Machine MigrationSSRN Electronic Journal10.2139/ssrn.4136053
https://doi.org/10.2139/ssrn.4136053

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