Skip to main content
SpringerLink
Log in

A practical cross-datacenter fault-tolerance algorithm in the cloud storage system

  • Published:
Cluster Computing Aims and scope Submit manuscript

Abstract

The fault-tolerance property in most cloud storage systems are designed within the scale of a single datacenter. The single datacenter as a whole may be unreachable or crashed due to severe problems, such as broken network links, power supply interruptions, and natural disasters, etc. Therefore, the design of an effective cross-datacenter fault-tolerant storage system is important to protect data security in the cloud. However, building a cross-datacenter fault-tolerant system faces great challenges, such as high latency, low throughput, high costs of bandwidth resources between datacenters. In this paper, we propose a practical cross-datacenter fault-tolerant (CDFT) algorithm in the cloud storage system. Our fault-tolerant algorithm design considers the difficult tradeoffs among fault tolerance, latency, throughput, network and storage costs. We propose the Domain Fault Codes (DFC) and the topology-aware scheduling techniques, which can tolerate the whole datacenter breakdown. We implemented the DFC-CDFT algorithm in a prototype cloud storage system. The experimental results showed that the proposed DFC-CDFT algorithm can effectively recover data blocks from the single datacenter failure while achieves low storage and bandwidth costs.

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

Access this article

Log in via an institution

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. Data Center Knowledge. UPDATE: Explosion in Downtown Los Angeles Disrupts Data Center Operations[N/OL]. http://www.datacenterknowledge.com/archives/2015/ 08/21/explosion-downtown-los-angeles-disrupts-data-center-operatio ns/. Accessed 15 Oct 2016

  2. Bailis, P., Davidson, A., Fekete, A., Ghodsi, A., Hellerstein, J.M., Stoica, I.: Highly available transactions: virtues and limitations. Proc. VLDB Endow. 7(3), 181–192 (2013)

    Article  Google Scholar 

  3. Greenberg, A., Hamilton, J., Maltz, D.A., Patel, P.: The cost of a cloud: research problems in data center networks. ACM SIGCOMM Comput. Commun. Rev. 39(1), 68–73 (2008)

    Article  Google Scholar 

  4. Shah, N.B., Lee, K., Ramchandran, K.: The MDS queue: analysing the latency performance of erasure codes. In: Proceedings of International Symposium on Information Theory (2014)

  5. Bailis, P.: Communication Costs in Real-world Networks [R/OL]. http://www.bailis.org/blog/communication-costs-in-real-world-networks/. Accessed 16 Oct 2016

  6. Ghemawat, S., Gobioff, H., Leung, S.-T.: The Google file system. ACM SIGOPS Oper. Syst. Rev. ACM 37(5), 29–43 (2003)

    Article  Google Scholar 

  7. Sivasubramanian, S.: Amazon dynamoDB: a seamlessly scalable non-relational database service. In: Proceedings of the 2012 ACM SIGMOD International Conference on Management of Data, pp. 729–730. ACM (2012)

  8. Huang, C., Simitci, H., Xu, Y., Ogus, A., Calder, B., Gopalan, P., Li, J., Yekhanin, S.: Erasure coding in Windows Azure storage. In: Proceedings of USENIX Annual Technical Conference (2012)

  9. Fikes, A.: Storage architecture and challenges. Talk at the Google Faculty Summit (2010)

  10. Shvachko, K., Kuang, H., Radia, S., Chansler, R.: The hadoop distributed file system[C]. In: IEEE 26th Symposium on Mass Storage Systems and Technologies (MSST), pp. 1–10. IEEE (2010)

  11. Dian Fu, Avik Key.: HDFS-5442: Zero loss HDFS data replication for multiple datacenters[EB/OL]. https://issues.apache.org/jira/browse/HDFS-5442. Accessed 16 Oct 2016

  12. Zhang, Z., Jiang, W.: HDFS-7285: Erasure Coding Support inside HDFS[EB/OL]. https://issues.apache.org/jira/browse/HDFS-7285. Accessed 16 Oct 2016

  13. The Apache Software Foundation.: HDFS-RAID Wiki[EB/OL]. http://wiki.apache.org/hadoop/HDFS-RAID. Accessed 16 Oct 2016

  14. Fan, B., Tantisiriroj, W., Xiao, L., Gibson, G.: DiskReduce: RAID for data-intensive scalable computing. In: Proceedings of the 4th Annual Workshop on Petascale Data Storage, pp. 6–10. ACM (2009)

  15. Sathiamoorthy, M., Asteris, M., Papailiopoulos, D., Dimakis, A.G., Vadali, R., Chen, S., Borthakur, D.: Xoring elephants: novel erasure codes for big data. Proc. VLDB Endow. VLDB Endow. 6(5), 325–336 (2013)

    Article  Google Scholar 

  16. Amazon Web Services, Inc.: Amazon DynamoDB Developer Guide: Cross-Region Replication Using DynamoDB Streams[R/OL]. http://docs.aws.amazon.com/amazondynamodb/latest/developerguide/Streams.CrossRegionRepl.html. Accessed 16 Oct 2016

  17. The Apache Software Foundation.: Apache Cassandra. http://cassandra.apache.org/. Accessed 16 Oct 2016

  18. Chang, F., Dean, J., Ghemawat, S., Hsieh, W.C., Wallach, D.A., Burrows, M., Chandra, T., Fikes, A., Gruber, R.E.: Bigtable: a distributed storage system for structured data. ACM Trans. Comput. Syst. (TOCS) 26(2), 4 (2008)

    Article  Google Scholar 

  19. Baker, J., Bond, C., Corbett, J.C., Furman, J.J., Khorlin, A., Larson, J., Leon, J.-M., Li, Y., Lloyd, A., Yushprakh, V.: Megastore: providing scalable, highly available storage for interactive services. CIDR 11, 223–234 (2011)

    Google Scholar 

  20. Corbett, J.C., Dean, J., Epstein, M., Fikes, A., Frost, C., Furman, J.J., Ghemawat, S., et al.: Spanner: Google’s globally distributed database. ACM Trans. Comput. Syst. (TOCS) 31(3), 8 (2013)

    Article  Google Scholar 

  21. Silberstein, M., Ganesh, L., Wang, Y., Alvisi, L., Dahlin, M.: Lazy means smart: reducing repair bandwidth costs in erasure-coded distributed storage. In: Proceedings of International Conference on Systems and Storage (2014)

  22. Huang, J., Liang, X., Qin, X., Xie, P., Xie, C.: Scale-RS: an efficient scaling scheme for RS-coded storage clusters. IEEE Trans. Parallel Distrib. Syst. 26(6), 1704–1717 (2015)

    Article  Google Scholar 

  23. Reed, I.S., Solomon, G.: Polynomial codes over certain finite fields. J. Soc. Ind. Appl. Math. 8(2), 300–304 (1960)

    Article  MathSciNet  MATH  Google Scholar 

  24. Galois field. https://en.wikipedia.org/wiki/Finite_field. Accessed 16 Oct 2016

  25. Rashmi, K.V., Nakkiran, P., Wang, J., Shah, N.B., Ramchandran, K.: Having Your Cake and Eating It Too: Jointly Optimal Erasure Codes for I/O, Storage, and Network-bandwidth. In: USENIX Conference on File and Storage Technologies (2015)

  26. Singh, A., Ong, J., Agarwal, A., Anderson, G.: Jupiter rising: a decade of clos topologies and centralized control in Google’s datacenter network. Commun. ACM 59(9), 88–97 (2016)

    Article  Google Scholar 

  27. Xiao, L., Ren, K., Zheng, Q., Gibson, G.A.: ShardFS vs. IndexFS: replication vs. caching strategies for distributed metadata management in cloud storage systems. In: Proceedings of the Sixth ACM Symposium on Cloud Computing (2015)

  28. Thomson, A., Abadi, D.J.: CalvinFS: consistent WAN replication and scalable metadata management for distributed file systems. In: Proceedings of the 13th USENIX Conference on File and Storage Technologies (2015)

  29. LevelDB. https://github.com/google/leveldb. Accessed 16 Oct 2016

  30. Ford, D., Labelle, F., Popovici, F.I., Stokely, M., Truong, V.-A., Barroso, L., Grimes, C., Quinlan, S.: Availability in Globally Distributed Storage Systems. In: Proceedings of the 9th USENIX Symposium on Operating Systems Design and Implementation, pp. 61–74 (2010)

  31. Standard, NIST-FIPS.: Announcing the advanced encryption standard (AES). Fed. Inf. Process. Stand. Publ. 197, 1–51 (2001)

Download references

Author information

Authors and Affiliations

  1. Deakin University, 221 Burwood Highway, Burwood, VIC, 3125, Australia

    Yuxia Cheng & Yang Xiang

  2. Zhejiang University, 38 Zheda Road, Xihu, Hangzhou, China

    Xinjie Yu & Wenzhi Chen

  3. State Key Laboratory of Mathematical Engineering and Advanced Computing, NO.62 Science Avenue, High-Tech Zone, Zhengzhou, China

    Rui Chang

Authors
  1. Yuxia Cheng
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Xinjie Yu
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Wenzhi Chen
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Rui Chang
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. Yang Xiang
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Yuxia Cheng.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Cheng, Y., Yu, X., Chen, W. et al. A practical cross-datacenter fault-tolerance algorithm in the cloud storage system. Cluster Comput 20, 1801–1813 (2017). https://doi.org/10.1007/s10586-017-0840-5

Download citation

  • Received:

  • Revised:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s10586-017-0840-5

Keywords

Search

Navigation