An analysis of live migration in openstack using high speed optical network

Biswas, Md Israfil, Parr, Gerard ORCID: https://orcid.org/0000-0002-9365-9132, McClean, Sally, Morrow, Philip and Scotney, Bryan (2016) An analysis of live migration in openstack using high speed optical network. In: Proceedings of 2016 SAI Computing Conference, SAI 2016. The Institute of Electrical and Electronics Engineers (IEEE), GBR, pp. 1267-1272. ISBN 9781467384605

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Abstract

Virtualisation technology has become a very common trend in modern datacentres as Virtual Machine (VM) migration brings several benefits like improved performance, high manageability, resource consolidation and fault tolerance. Live Migration (LM) of VMs is used for transferring a working VM from one host to another host of a different physical machine without interfering with the existing VMs. However, little research has been done in considering the real time resource consumption and latency of live VM migration that reduces these benefits to much less than their potential. In this paper, we present an analysis of LM in our unique TransAtlantic high speed optical fibre network connecting Northern Ireland, Dublin and Halifax (Canada). We show that the total migration times as well as total network data transfer for post-copy LM are both dominated by specific VM memory patterns using loaded or unloaded VMs. We also found that the downtime for different VM memory patterns is not extremely varied and no severe effect is experienced over our long distance network.

Item Type:	Book Section
Uncontrolled Keywords:	cloud,live migration,openstack,optical network,virtual machine,artificial intelligence,computer networks and communications,computer science applications,information systems,signal processing,modelling and simulation ,/dk/atira/pure/subjectarea/asjc/1700/1702
Faculty \ School:	Faculty of Science > School of Computing Sciences
UEA Research Groups:	Faculty of Science > Research Groups > Smart Emerging Technologies Faculty of Science > Research Groups > Cyber Security Privacy and Trust Laboratory Faculty of Science > Research Groups > Data Science and AI
Related URLs:	http://www.scopus.com/inward/record.url?...
Depositing User:	Pure Connector
Date Deposited:	31 Oct 2016 17:00
Last Modified:	10 Dec 2024 01:11
URI:	https://ueaeprints.uea.ac.uk/id/eprint/61188
DOI:	10.1109/SAI.2016.7556142

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