Live migration of virtual machines with their local persistent storage in a data intensive cloud

Abhinit Modi, Raghavendra Achar, P. Santhi Thilagam

Research output: Contribution to journalArticle

2 Citations (Scopus)

Abstract

Processing large volumes of data to drive their core business has been the primary objective of many firms and scientific applications in these days. Cloud computing being a large-scale distributed computing paradigm can be used to cater for the needs of data intensive applications. There are various approaches for managing the workload on a data intensive cloud. Live migration of a virtual machine is the most prominent paradigm. Existing approaches to live migration use network attached storage where just the run time state needs to be transferred. Live migration of virtual machines with local persistent storage has been shown to have performance advantages like security, availability and privacy. This paper presents an optimised approach for migration of a virtual machine along with its local storage by considering the locality of storage access. Count map combined with a restricted block transfer mechanism is used to minimise the downtime and overhead. The solution proposed is tested by various parameters like bandwidth, write access patterns and threshold. Results show the improvement in downtime and reduction in overhead.

Original languageEnglish
Pages (from-to)134-147
Number of pages14
JournalInternational Journal of High Performance Computing and Networking
Volume10
Issue number1-2
DOIs
Publication statusPublished - 2017

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Distributed computer systems
Cloud computing
Availability
Bandwidth
Processing
Virtual machine
Industry

All Science Journal Classification (ASJC) codes

  • Software
  • Hardware and Architecture
  • Computer Networks and Communications

Cite this

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Live migration of virtual machines with their local persistent storage in a data intensive cloud. / Modi, Abhinit; Achar, Raghavendra; Thilagam, P. Santhi.

In: International Journal of High Performance Computing and Networking, Vol. 10, No. 1-2, 2017, p. 134-147.

Research output: Contribution to journalArticle

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