Fair Comparison of DSS Codes

  • Natasa Paunkoska
  • Weiler A. FinamoreEmail author
  • Ninoslav Marina
Conference paper
Part of the Advances in Intelligent Systems and Computing book series (AISC, volume 886)


The use of a distributed storage system (DSS), in a network with a large number of interconnected nodes, can increase significantly the storage efficiency. The main issues in this area are the reconstruction process or obtaining the entire original message out of the DSS and the repair process or recovering the lost stored data of a failed node. Finding an adequate code that will manage successfully both processes, to have an efficient system, is a challenge. Many codes for data distribution are in play. Finding a good code (good encoding procedure), one which achieves better performance, require choosing ways to make a fair comparison among codes. In this paper, we are proposing a way to compare two DSS codes by examining the system parameters. We conclude by comparing three different class of codes: Repetition, Reed-Solomon and Regenerating codes and deciding which one is the most efficient.


Distributed storage system (DSS) Reconstruction process Repair process Efficient DSS system 


  1. 1.
    Moon, T.K.: Error Correction Coding: Mathematical Methods and Algorithms, 1 edn. Wiley-Interscience (2005)Google Scholar
  2. 2.
    Weatherspoon, H., Kubiatowicz, J.: Erasure coding vs. replication: a quantitative comparison. In: Proceedings of 1st International Workshop Peer-to-Peer System (IPTPS), pp. 328–338 (2007)CrossRefGoogle Scholar
  3. 3.
    Memorandum of understanding for the implementation of the COST Action “European Cooperation for Statistics of Network Data Science” (2015)Google Scholar
  4. 4.
    Rashmi, K.V., Shah, N.B., Kumar, P.V.: Regenerating codes for errors and erasures in distributed storage. In: Proceedings of IEEE International Symposium on Information Theory (ISIT) (2012)Google Scholar
  5. 5.
    Dimakis, A.G., Prabhakaran, V., Ramchandran, K.: Decentralized erasure code for distributed networked storage. IEEE/ACM Trans. Netw. 14, 2809 (2006)MathSciNetCrossRefGoogle Scholar
  6. 6.
    Rawat, A.S., Koyluoglu, O.O., Silberstein, N., Vishwanath, S.: Optimal locally repairable and secure codes for distributed storage system. IEEE Trans. Inf. Theory 60, 212–236 (2013)CrossRefGoogle Scholar
  7. 7.
    Dimakis, A.G., Godfrey, P.B., Wainright, M., Ramchadran, K.: Network coding for distributed storage systems. In: Proceedings of the 26th IEEE International Conference on Computer Communications, Anchorage, AK, pp. 2000–2008, May 2007Google Scholar
  8. 8.
    Dimakis, A.G., Godfrey, P.B., Wu, Y., Wainright, M.J., Ramchandran, K.: Network coding for distributed storage systems. IEEE Trans. Inf. Theory 57(8), 5227–5239 (2011)Google Scholar
  9. 9.
    Rashmi, K.V., Shah, N.B., Kumar, P.V.: Optimal exact-regenerating codes for distributed storage at the MSR and MBR points via a product-Matrix Construction. IEEE Trans. Inf. Theory 57(8), 5227–5239 (2011)MathSciNetCrossRefGoogle Scholar
  10. 10.
    Paunkoska, N., Finamore, W., Karamachoski, J., Puncheva, M., Marina, N.: Improving DSS efficiency with shortened MSR codes. In: ICUMT (2016)Google Scholar

Copyright information

© Springer Nature Switzerland AG 2019

Authors and Affiliations

  • Natasa Paunkoska
    • 1
  • Weiler A. Finamore
    • 1
    Email author
  • Ninoslav Marina
    • 1
  1. 1.University of Information Systems and Technology St. Paul the Apostol (UIST)OhridMacedonia

Personalised recommendations