Skip to main content
Log in

E2FS: an elastic storage system for cloud computing

  • Published:
The Journal of Supercomputing Aims and scope Submit manuscript

Abstract

In cloud storage, replication technologies are essential to fault tolerance and high availability of data. While achieving the goal of high availability, replication brings extra number of active servers to the storage system. Extra active servers mean extra power consumption and capital expenditure. Furthermore, the lack of classification of data makes replication scheme fixed at the very beginning. This paper proposes an elastic and efficient file storage called E2FS for big data applications. E2FS can dynamically scale in/out the storage system based on real-time demands of big data applications. We adopt a novel replication scheme based on data blocks, which provides a fine-grained maintenance of the data in the storage system. E2FS analyzes features of data and makes dynamic replication decision to balance the cost and performance of cloud storage. To evaluate the performance of proposed work, we implement a prototype of E2FS and compare it with HDFS. Our experiments show E2FS can outperform HDFS in elasticity while achieving guaranteed performance for big data applications.

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

Access this article

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

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6

Similar content being viewed by others

References

  1. Chen M, Hai J, Wen Y, Leung VC (2013) Enabling technologies for future data center networking: a primer. IEEE Netw 27(4):8–15

    Article  Google Scholar 

  2. Li J, Qiu M, Niu J, Gao W, Zong Z, Qin X (2010) Feedback dynamic algorithms for preemptable job scheduling in cloud systems. In: Proceedings of the IEEE/WIC/ACM International Conference on Web Intelligence, DC, USA, pp 561–564

  3. Dai W, Qiu M (2015) Energy optimization with dynamic task scheduling mobile cloud computing. Syst J IEEE PP(99):1–10

  4. Chen M, Mao S, Zhang Y, Leung VC (2014) Big data: related technologies, challenges and future prospects. Springer Briefs in Computer Science

  5. Zhang Y, Chen M, Mao S, Hu L, Leung VC (2014) Cap: Community activity prediction based on big data analysis. IEEE Netw 28(4):52–57

    Article  Google Scholar 

  6. Chen M, Hao Y, Li Y, Lai C, Wu D (2015) On the computation offloading at ad hoc cloudlet: architecture and service modes. IEEE Commun Mag 53(6):18–24

    Article  Google Scholar 

  7. Cidon A et al (2013) Copysets: reducing the frequency of data loss in cloud storage. In: USENIX Annual Technical Conference 2013 (USENIXATC 13). San Jose, pp 37–48

  8. Qiu M, Ming Z (2013) Informer homed routing fault tolerance mechanism for wireless sensor networks. J Syst Archit 59(4):260–270

    Article  Google Scholar 

  9. CISCO (2014) Cisco Visual Networking Index: Forecast and Methodology, 2014–2019 White Paper. http://www.cisco.com/c/en/us/solutions/collateral/service-provider/ip-ngn-ip-next-generation-network/white_paper_c11-481360.html. Accessed 18 Feb 2016

  10. CNET (2013) Cloud storage comparison. http://www.cnet.com/how-to/onedrive-dropbox-google-drive-and-box-which-cloud-storage-service-is-right-for-you/. Accessed 18 Feb 2016

  11. Gai K, Qiu M (2015) Dynamic Energy-aware Cloudlet-based Mobile Cloud Computing Model for Green Computing. J Netw Comput Appl 59:46–54

  12. Wu G, Qiu M (2013) A decentralized approach for mining event correlations in dis- tributed system monitoring. J Parallel Distrib Comput 73(3):330–340

    Article  MathSciNet  Google Scholar 

  13. Xu L et al (2014) SpringFS: bridging agility and performance in elastic distributed storage. In: Proceedings of the 12th USENIX Conference on File and Storage Technologies (FAST 14). Santa Clara, CA, pp 243–255

  14. Harter T et al (2014) Analysis of hdfs under hbase: A facebook messages case study. In: Proceedings of the 12th USENIX Conference on File and Storage Technologies (FAST 14), pp 199–212

  15. Wang H, Varman P (2014) Balancing fairness and effciency in tiered storage systems with bottleneck-aware allocation. In: Proceedings of the 12th USENIX Conferenceon File and Storage Technologies (FAST 14), pp 229–242

  16. Cidon A et al (2015) Tiered replication: a cost-effective alternative to full cluster geo-replication. In: 2015 USENIX Annual Technical Conference (USENIX ATC 15), pp 31–43

  17. Bowers KD, Juels A, Oprea A (2009) Hail: a high-availability and integrity layer for cloud storage. In: Proceedings of the 16th ACM Conference on Computer and Communications Security. ACM, New York, pp 187–198

Download references

Acknowledgments

This work is supported by NSF CNS-1457506 and NSF CNS-1359557.

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to Longbin Chen.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Chen, L., Qiu, M., Song, J. et al. E2FS: an elastic storage system for cloud computing. J Supercomput 74, 1045–1060 (2018). https://doi.org/10.1007/s11227-016-1827-3

Download citation

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s11227-016-1827-3

Keywords

Navigation