research-article

Workload-aware live storage migration for clouds

Authors:

Tze Sing Eugene Ng,

Kunwadee SripanidkulchaiAuthors Info & Claims

ACM SIGPLAN Notices, Volume 46, Issue 7

Pages 133 - 144

https://doi.org/10.1145/2007477.1952700

Published: 09 March 2011 Publication History

Abstract

The emerging open cloud computing model will provide users with great freedom to dynamically migrate virtualized computing services to, from, and between clouds over the wide-area. While this freedom leads to many potential benefits, the running services must be minimally disrupted by the migration. Unfortunately, current solutions for wide-area migration incur too much disruption as they will significantly slow down storage I/O operations during migration. The resulting increase in service latency could be very costly to a business. This paper presents a novel storage migration scheduling algorithm that can greatly improve storage I/O performance during wide-area migration. Our algorithm is unique in that it considers individual virtual machine's storage I/O workload such as temporal locality, spatial locality and popularity characteristics to compute an efficient data transfer schedule. Using a fully implemented system on KVM and a trace-driven framework, we show that our algorithm provides large performance benefits across a wide range of popular virtual machine workloads.

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Cited By

Li PCao J(2023)Efficient online migration mechanism for memory write-intensive virtual machinesJournal of Cloud Computing10.1186/s13677-023-00433-712:1Online publication date: 6-Apr-2023
https://doi.org/10.1186/s13677-023-00433-7
Shi BShen HDong BZheng Q(2022)Memory/Disk Operation Aware Lightweight VM Live MigrationIEEE/ACM Transactions on Networking10.1109/TNET.2022.315593530:4(1895-1910)Online publication date: Aug-2022
https://doi.org/10.1109/TNET.2022.3155935
Choi SPark K(2022) iContainerJournal of Network and Computer Applications10.1016/j.jnca.2022.103494208:COnline publication date: 1-Dec-2022
https://dl.acm.org/doi/10.1016/j.jnca.2022.103494
Show More Cited By

Index Terms

Workload-aware live storage migration for clouds
1. Software and its engineering
  1. Software organization and properties
    1. Contextual software domains
      1. Operating systems

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Information

Published In

cover image ACM SIGPLAN Notices

ACM SIGPLAN Notices Volume 46, Issue 7

VEE '11

July 2011

231 pages

ISSN:0362-1340

EISSN:1558-1160

DOI:10.1145/2007477

Issue’s Table of Contents

VEE '11: Proceedings of the 7th ACM SIGPLAN/SIGOPS international conference on Virtual execution environments
March 2011
250 pages
ISBN:9781450306874
DOI:10.1145/1952682
General Chair:
Erez Petrank
The Technion, Israel
,
Program Chair:
Doug Lea
SUNY Oswego, USA

Copyright © 2011 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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Association for Computing Machinery

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Publication History

Published: 09 March 2011

Published in SIGPLAN Volume 46, Issue 7

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Cited By

Li PCao J(2023)Efficient online migration mechanism for memory write-intensive virtual machinesJournal of Cloud Computing10.1186/s13677-023-00433-712:1Online publication date: 6-Apr-2023
https://doi.org/10.1186/s13677-023-00433-7
Shi BShen HDong BZheng Q(2022)Memory/Disk Operation Aware Lightweight VM Live MigrationIEEE/ACM Transactions on Networking10.1109/TNET.2022.315593530:4(1895-1910)Online publication date: Aug-2022
https://doi.org/10.1109/TNET.2022.3155935
Choi SPark K(2022) iContainerJournal of Network and Computer Applications10.1016/j.jnca.2022.103494208:COnline publication date: 1-Dec-2022
https://dl.acm.org/doi/10.1016/j.jnca.2022.103494
El Kafhali SEl Mir IHanini M(2021)Security Threats, Defense Mechanisms, Challenges, and Future Directions in Cloud ComputingArchives of Computational Methods in Engineering10.1007/s11831-021-09573-yOnline publication date: 7-Apr-2021
https://doi.org/10.1007/s11831-021-09573-y
Pang LAlazzawe AKant KSwift J(2019)Data Heat Prediction in Storage Systems Using Behavior Specific Prediction Models2019 IEEE 38th International Performance Computing and Communications Conference (IPCCC)10.1109/IPCCC47392.2019.8958715(1-8)Online publication date: Oct-2019
https://doi.org/10.1109/IPCCC47392.2019.8958715
Li CFeng DHua YQin L(2019)Efficient live virtual machine migration for memory write-intensive workloadsFuture Generation Computer Systems10.1016/j.future.2018.12.048Online publication date: Jan-2019
https://doi.org/10.1016/j.future.2018.12.048
Nayyer MRaza IHussain S(2019)Revisiting VM performance and optimization challenges for big data10.1016/bs.adcom.2019.02.002Online publication date: 2019
https://doi.org/10.1016/bs.adcom.2019.02.002
Zhang FLiu GFu XYahyapour R(2018)A Survey on Virtual Machine Migration: Challenges, Techniques, and Open IssuesIEEE Communications Surveys & Tutorials10.1109/COMST.2018.279488120:2(1206-1243)Online publication date: Oct-2019
https://doi.org/10.1109/COMST.2018.2794881
Patel R(2018)Consolidation in Cloud Environment Using Optimization TechniquesCloud Computing for Optimization: Foundations, Applications, and Challenges10.1007/978-3-319-73676-1_6(123-151)Online publication date: 27-Feb-2018
https://doi.org/10.1007/978-3-319-73676-1_6
Pelluri SSirandas R(2017)Energy Saving in Cloud by Using Enhanced Instance Based Learning (EIBL) for Resource PredictionSustainable Cloud and Energy Services10.1007/978-3-319-62238-5_7(167-201)Online publication date: 21-Sep-2017
https://doi.org/10.1007/978-3-319-62238-5_7
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