Location via proxy:   [ UP ]  
[Report a bug]   [Manage cookies]                
Skip to main content
SpringerLink
Log in

ENTICE VM Image Analysis and Optimised Fragmentation

  • Published:
Journal of Grid Computing Aims and scope Submit manuscript

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.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Subscribe and save

Springer+ Basic
$34.99 /Month
  • Get 10 units per month
  • Download Article/Chapter or eBook
  • 1 Unit = 1 Article or 1 Chapter
  • Cancel anytime
Subscribe now

Buy Now

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Similar content being viewed by others

References

  1. Amazon Web Services: Amazon Elastic Compute Cloud (Amazon EC2). https://aws.amazon.com/ec2/ (2017)

  2. Brown, N.: Linux kernel storage overlayfs driver. https://git.kernel.org/cgit/linux/kernel/git/torvalds/linux.git/tree/Documentation/filesystems/overlayfs.txt (2016)

  3. BTRFS: Wiki. https://btrfs.wiki.kernel.org/index.php/Main_Page (2016)

  4. Canonical: Introduction to linux containers. https://linuxcontainers.org/lxc/introduction/ (2016)

  5. Canonical: Linux containers: what is lxd? https://linuxcontainers.org/lxd/ (2016)

  6. Canonical: Lxd 2.0 image management. https://insights.ubuntu.com/2016/04/01/lxd-2-0-image-management-512/ (2016)

  7. Canonical: cloud-init - the standard for customising cloud instances. https://cloud-init.io/ (2017)

  8. Canonical: Ubuntu cloud images 16.04 lts daily build. https://cloud-images.ubuntu.com/xenial/ (2017)

  9. Docker: Storage overlayfs driver. https://docs.docker.com/engine/userguide/storagedriver/overlayfs-driver/ (2016)

  10. Dyck, A., Penners, R., Lichter, H.: Towards definitions for release engineering and devops. In: Proceedings of the Third International Workshop on Release Engineering, pp. 3–3. IEEE Press, Piscataway (2015)

  11. ENTICE: project website. http://www.entice-project.eu/ (2017)

  12. ENTICE: Wp3 github repository. https://github.com/entice-repository/wp3-image-synthesis (2017)

  13. Flexiant: Flexiant cloud orchestrator (fco). https://www.flexiant.com/flexiant-cloud-orchestrator/ (2017)

  14. Gec, S., Kimovski, D., Prodan, R., Stankovski, V.: Using constraint-based reasoning for multi-objective optimisation of the entice environment. In: 2016 12th International Conference on Semantics, Knowledge and Grids (SKG), pp. 17–24 (2016)

  15. Geer, D.: The os faces a brave new world. Computer 42(10), 15–17 (2009)

    Article  Google Scholar 

  16. Hajnal, Á., Márton, I., Farkas, Z., Kacsuk, P.: Remote storage management in science gateways via data brid- ging. Concurr. Comput.: Pract. Exp. 27(16), 4398–4411 (2015)

    Article  Google Scholar 

  17. Hovestadt, M., Kao, O., Kliem, A., Warneke, D.: Adaptive online compression in clouds—making informed decisions in virtual machine environments. J. Grid Comput. 11(2), 167–186 (2013)

    Article  Google Scholar 

  18. Jayaram, K., Peng, C., Zhang, Z., Kim, M., Chen, H., Lei, H.: An empirical analysis of similarity in virtual machine images. In: Proceedings of the Middleware 2011 Industry Track Workshop, p 6. ACM (2011)

  19. Jin, K., Miller, E.L.: The effectiveness of deduplication on virtual machine disk images. In: Proceedings of SYSTOR 2009: The Israeli Experimental Systems Conference, p. 7. ACM (2009)

  20. Kimovski, D., Saurabh, N., Stankovski, V., Prodan, R.: Multi-objective middleware for distributed vmi repositories in federated cloud environment. Scalable Comput.: Pract. Exp. 17(4), 299–312 (2016)

    Google Scholar 

  21. Kimovski, D., Marosi, A., Gec, S., Saurabh, N., Kertesz, A., Kecskemeti, G., Stankovski, V., Prodan, R.: Distributed environment for efficient virtual machine image management in federated cloud architectures. Concurr. Comput.: Pract. Exp. e4220-n/a (2017)

  22. Kováas, J., Kacsuk, P.: Occopus: a multi-cloud orchestrator to deploy and manage complex scientific infrastructures. J. Grid Comput 16(1), 19–37 (2018)

    Article  Google Scholar 

  23. Lagar-Cavilla, H.A., Whitney, J.A., Scannell, A.M., Patchin, P., Rumble, S.M., De Lara, E., Brudno, M., Satyanarayanan, M.: Snowflock: rapid virtual machine cloning for cloud computing. In: Proceedings of the 4th ACM European Conference on Computer Systems, pp. 1–12. ACM (2009)

  24. Lin, X., Hibler, M., Eide, E., Ricci, R.: Using deduplicating storage for efficient disk image deployment. EAI Endorsed Trans. Scalable Inf. Syst. 2(6), e1 (2015)

    Google Scholar 

  25. Marosi, A.C.: List of fragments for “entice vm image analysis and optimised fragmentation”. https://s3.lpds.sztaki.hu/atisu/papers/entice-fragmentation/fragments.pdf (2017)

  26. Milojicic, D., Llorente, I.M., Montero, R.S.: Opennebula: a cloud management tool. IEEE Internet Comput. 15(2), 11–14 (2011)

    Article  Google Scholar 

  27. Peinl, R., Holzschuher, F., Pfitzer, F.: Docker cluster management for the cloud-survey results and own solution. J. Grid Comput. 14(2), 265–282 (2016)

    Article  Google Scholar 

  28. Rkt: the pod-native container engine. https://github.com/rkt/rkt (2017)

  29. Virtuozzo: Openvz virtuozzo containers wiki. https://openvz.org/Virtuozzo (2016)

  30. Xu, J., Zhang, W., Zhang, Z., Wang, T., Huang, T.: Clustering-based acceleration for virtual machine image deduplication in the cloud environment. J. Syst. Softw. 121, 144–156 (2016)

    Article  Google Scholar 

  31. Zhao, X., Zhang, Y., Wu, Y., Chen, K., Jiang, J., Li, K.: Liquid: a scalable deduplication file system for virtual machine images. IEEE Trans. Parallel Distrib. Syst. 25(5), 1257–1266 (2014)

    Article  Google Scholar 

Download references

Funding

This research work has received funding from the European Union’s Horizon 2020 research and innovation program under grant agreement No 644179 (ENTICE).

Author information

Authors and Affiliations

  1. Institute for Computer Science and Control, Hungarian Academy of Sciences, Budapest, Hungary

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

  2. Department of Computer Science, Liverpool John Moores University, Liverpool, UK

    Gabor Kecskemeti

Authors
  1. Akos Hajnal
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Gabor Kecskemeti
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Attila Csaba Marosi
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Jozsef Kovacs
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. Peter Kacsuk
    View author publications

    You can also search for this author in PubMed Google Scholar

  6. Robert Lovas
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Attila Csaba Marosi.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Hajnal, A., Kecskemeti, G., Marosi, A.C. et al. ENTICE VM Image Analysis and Optimised Fragmentation. J Grid Computing 16, 247–263 (2018). https://doi.org/10.1007/s10723-018-9430-x

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s10723-018-9430-x

Keywords

Search

Navigation