ENTICE VM Image Analysis and Optimised Fragmentation

Akos Hajnal, Gabor Kecskemeti, Attila Csaba Marosi, Jozsef Kovacs, Peter Kacsuk, Robert Lovas

Research output: Contribution to journalArticle


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.

Original languageEnglish
Pages (from-to)247-263
Number of pages17
JournalJournal of Grid Computing
Issue number2
Publication statusPublished - Jun 1 2018



  • Cloud storage
  • Image de-duplication
  • Image fragmentation
  • Storage optimization
  • Virtual machine image

ASJC Scopus subject areas

  • Software
  • Information Systems
  • Hardware and Architecture
  • Computer Networks and Communications

Cite this

Hajnal, A., Kecskemeti, G., Marosi, A. C., Kovacs, J., Kacsuk, P., & Lovas, R. (2018). ENTICE VM Image Analysis and Optimised Fragmentation. Journal of Grid Computing, 16(2), 247-263. https://doi.org/10.1007/s10723-018-9430-x