Hdl Handle:
http://hdl.handle.net/10545/620877
Title:
iMIG: Toward an adaptive live migration method for KVM virtual machines
Authors:
Li, Jianxin; Zhao, Jieyu Zhao; Li, Yi; Cui, Lei; Li, Bo; Liu, Lu ( 0000-0003-1013-4507 ) ; Panneerselvam, John ( 0000-0002-0332-1681 )
Abstract:
With the energy and power costs increasing alongside the growth of the IT infrastructures, achieving workload concentration and high availability in cloud computing environments is becoming more and more complex. Virtual machine (VM) migration has become an important approach to address this issue, particularly; live migration of the VMs across the physical servers facilitates dynamic workload scheduling of the cloud services as per the energy management requirements, and also reduces the downtime by allowing the migration of the running instances. However, migration is a complex process affected by several factors such as bandwidth availability, application workload and operating system configurations, which in turn increases the complications in predicting the migration time in order to negotiate the service-level agreements in a real datacenter. In this paper, we propose an adaptive approach named improved MIGration (iMIG), in which we characterize some of the key metrics of the live migration performance, and conduct several experiments to study the impacts of the investigated metrics on the Kernel-based VM (KVM) functionalities, as well as the energy consumed by both the destination and the source hosts. Our results reveal the importance of the configured parameters: speed limit, TCP buffer size and max downtime, along with the VM properties and also their corresponding impacts on the migration process. Improper setting of these parameters may either incur migration failures or causes excess energy consumption. We witness a few bugs in the existing Quick EMUlator (QEMU)/KVM parameter computation framework, which is one of most widely used KVM frameworks based on QEMU. Based on our observations, we develop an analytical model aimed at better predictions of both the migration time and the downtime, during the process of VM deployment. Finally, we implement a suite of profiling tools in the adaptive mechanism based on the qemu-kvm-0.12.5 version, and our experiment results prove the efficiency of our approach in improving the live migration performance. In comparison with the default migration approach, our approach achieves a 40% reduction in the migration latency and a 45% reduction in the energy consumption.
Affiliation:
University of Derby
Citation:
Li, J. et al (2014) 'iMIG: Toward an Adaptive Live Migration Method for KVM Virtual Machines', The Computer Journal, 58 (6):1227
Publisher:
Oxford University Press
Journal:
The Computer Journal
Issue Date:
22-Jul-2014
URI:
http://hdl.handle.net/10545/620877
DOI:
10.1093/comjnl/bxu065
Additional Links:
http://comjnl.oxfordjournals.org/cgi/doi/10.1093/comjnl/bxu065
Type:
Article
Language:
en
ISSN:
0010-4620
EISSN:
1460-2067
Appears in Collections:
Department of Electronics, Computing & Maths

Full metadata record

DC FieldValue Language
dc.contributor.authorLi, Jianxinen
dc.contributor.authorZhao, Jieyu Zhaoen
dc.contributor.authorLi, Yien
dc.contributor.authorCui, Leien
dc.contributor.authorLi, Boen
dc.contributor.authorLiu, Luen
dc.contributor.authorPanneerselvam, Johnen
dc.date.accessioned2016-11-16T18:21:38Z-
dc.date.available2016-11-16T18:21:38Z-
dc.date.issued2014-07-22-
dc.identifier.citationLi, J. et al (2014) 'iMIG: Toward an Adaptive Live Migration Method for KVM Virtual Machines', The Computer Journal, 58 (6):1227en
dc.identifier.issn0010-4620-
dc.identifier.doi10.1093/comjnl/bxu065-
dc.identifier.urihttp://hdl.handle.net/10545/620877-
dc.description.abstractWith the energy and power costs increasing alongside the growth of the IT infrastructures, achieving workload concentration and high availability in cloud computing environments is becoming more and more complex. Virtual machine (VM) migration has become an important approach to address this issue, particularly; live migration of the VMs across the physical servers facilitates dynamic workload scheduling of the cloud services as per the energy management requirements, and also reduces the downtime by allowing the migration of the running instances. However, migration is a complex process affected by several factors such as bandwidth availability, application workload and operating system configurations, which in turn increases the complications in predicting the migration time in order to negotiate the service-level agreements in a real datacenter. In this paper, we propose an adaptive approach named improved MIGration (iMIG), in which we characterize some of the key metrics of the live migration performance, and conduct several experiments to study the impacts of the investigated metrics on the Kernel-based VM (KVM) functionalities, as well as the energy consumed by both the destination and the source hosts. Our results reveal the importance of the configured parameters: speed limit, TCP buffer size and max downtime, along with the VM properties and also their corresponding impacts on the migration process. Improper setting of these parameters may either incur migration failures or causes excess energy consumption. We witness a few bugs in the existing Quick EMUlator (QEMU)/KVM parameter computation framework, which is one of most widely used KVM frameworks based on QEMU. Based on our observations, we develop an analytical model aimed at better predictions of both the migration time and the downtime, during the process of VM deployment. Finally, we implement a suite of profiling tools in the adaptive mechanism based on the qemu-kvm-0.12.5 version, and our experiment results prove the efficiency of our approach in improving the live migration performance. In comparison with the default migration approach, our approach achieves a 40% reduction in the migration latency and a 45% reduction in the energy consumption.en
dc.language.isoenen
dc.publisherOxford University Pressen
dc.relation.urlhttp://comjnl.oxfordjournals.org/cgi/doi/10.1093/comjnl/bxu065en
dc.rightsArchived with thanks to The Computer Journalen
dc.rights.urihttp://creativecommons.org/licenses/by-nc-nd/4.0/en
dc.subjectLive migrationen
dc.subjectEnergy savingen
dc.subjectCloud computingen
dc.subjectAdaptive modelen
dc.titleiMIG: Toward an adaptive live migration method for KVM virtual machinesen
dc.typeArticleen
dc.identifier.eissn1460-2067-
dc.contributor.departmentUniversity of Derbyen
dc.identifier.journalThe Computer Journalen
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