ENTICE VM image analysis and optimised fragmentation

Kecskemeti, G, Hajnal, A, Marosi, AC, Kovacs, J, Kacsuk, P and Lovas, R (2018) ENTICE VM image analysis and optimised fragmentation. Journal of Grid Computing, 16 (2). pp. 247-263. ISSN 1570-7873

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Abstract

Virtual machine (VM) images (VMIs) often share common parts of significant size as they are stored individually. Using existing de-duplication techniques for such images are non-trivial, impose serious technical challenges, and requires direct access to clouds' proprietary image storages, which is not always feasible. We propose an alternative approach to split images into shared parts, called fragments, which are stored only once. Our solution requires a reasonably small set of base images available in the cloud, and additionally only the increments will be stored without the contents of base images, providing significant storage space savings. Composite images consisting of a base image and one or more fragments are assembled on- demand at VM deployment. Our technique can be used in conjunction with practically any popular cloud solution, and the storage of fragments is independent of the proprietary image storage of the cloud provider.

Item Type:	Article
Additional Information:	This is a post-peer-review, pre-copyedit version of an article published in Journal of Grid Computing. The final authenticated version is available online at: http://dx.doi.org/10.1007/s10723-018-9430-x
Uncontrolled Keywords:	0805 Distributed Computing
Subjects:	Q Science > QA Mathematics > QA75 Electronic computers. Computer science
Divisions:	Computer Science and Mathematics
Publisher:	Springer Verlag
Date of acceptance:	31 January 2018
Date of first compliant Open Access:	26 March 2019
Date Deposited:	09 Feb 2018 11:06
Last Modified:	04 Sep 2021 03:17
URI:	https://researchonline.ljmu.ac.uk/id/eprint/7977

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