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Live gang migration of virtual machines

Published: 08 June 2011 Publication History

Abstract

This paper addresses the problem of simultaneously migrating a group of co-located and live virtual machines (VMs), i.e, VMs executing on the same physical machine. We refer to such a mass simultaneous migration of active VMs as "live gang migration". Cluster administrators may often need to perform live gang migration for load balancing, system maintenance, or power savings. Application performance requirements may dictate that the total migration time, network traffic overhead, and service downtime, be kept minimal when migrating multiple VMs. State-of-the-art live migration techniques optimize the migration of a single VM. In this paper, we optimize the simultaneous live migration of multiple co-located VMs. We present the design, implementation, and evaluation of a de-duplication based approach to perform concurrent live migration of co-located VMs. Our approach transmits memory content that is identical across VMs only once during migration to significantly reduce both the total migration time and network traffic. Using the QEMU/KVM platform, we detail a proof-of-concept prototype implementation of two types of de-duplication strategies (at page level and sub-page level) and a differential compression approach to exploit content similarity across VMs. Evaluations over Gigabit Ethernet with various types of VM workloads demonstrate that our prototype for live gang migration can achieve significant reductions in both network traffic and total migration time.

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cover image ACM Conferences
HPDC '11: Proceedings of the 20th international symposium on High performance distributed computing
June 2011
296 pages
ISBN:9781450305525
DOI:10.1145/1996130
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: 08 June 2011

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Author Tags

  1. live migration
  2. operating systems
  3. virtual machines

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  • (2023)Template-Aware Live Migration of Virtual MachinesProceedings of the Eighth ACM/IEEE Symposium on Edge Computing10.1145/3583740.3626812(336-340)Online publication date: 6-Dec-2023
  • (2023)Virtual machine migration based algorithmic approach for safeguarding environmental sustainability by renewable energy usage maximization in Cloud data centresInternational Journal of Information Technology10.1007/s41870-023-01478-2Online publication date: 16-Sep-2023
  • (2022)Optimization of SLA aware live migration of multiple virtual machines using Lagrange multiplierFuture Generation Computer Systems10.1016/j.future.2022.01.001130:C(279-291)Online publication date: 1-May-2022
  • (2022)Overview of Data DeduplicationData Deduplication for High Performance Storage System10.1007/978-981-19-0112-6_2(9-23)Online publication date: 3-Jun-2022
  • (2022)Safeguarding Cloud Services Sustainability by Dynamic Virtual Machine Migration with Re-allocation Oriented Algorithmic ApproachSmart Trends in Computing and Communications10.1007/978-981-16-9967-2_40(425-435)Online publication date: 6-Jul-2022
  • (2022)IPAM: An Improved Penalty Aware VM Migration TechniqueComputational Science and Its Applications – ICCSA 202210.1007/978-3-031-10522-7_23(324-336)Online publication date: 15-Jul-2022
  • (2021)Exploiting Sub-page Write Protection for VM Live Migration2021 IEEE 14th International Conference on Cloud Computing (CLOUD)10.1109/CLOUD53861.2021.00063(484-490)Online publication date: Sep-2021
  • (2020)DeepClone: Lightweight State Replication of Deep Learning Models for Data Parallel Training2020 IEEE International Conference on Cluster Computing (CLUSTER)10.1109/CLUSTER49012.2020.00033(226-236)Online publication date: Sep-2020
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