skip to main content
research-article

Liquid Cloud Storage

Published:18 February 2019Publication History
Skip Abstract Section

Abstract

A liquid system provides durable object storage based on spreading redundantly generated data across a network of hundreds to thousands of potentially unreliable storage nodes. A liquid system uses a combination of a large code, lazy repair, and flow storage organization. We show that a liquid system can be operated to enable flexible and essentially optimal combinations of storage durability, storage overhead, repair bandwidth usage, and access performance.

References

  1. Backblaze. 2016. Hard Drive Data and Stats. Retrieved October 8, 2018, from https://www.backblaze.com/b2/hard-drive-test-data.html.Google ScholarGoogle Scholar
  2. M. Belshe, R. Peon, and M. Thomson (Eds.). 2015. Hypertext Transfer Protocol Version 2 (HTTP/2), RFC 7540. Retrieved on May 2015 from https://www.rfc-editor.org/info/rfc7540.Google ScholarGoogle Scholar
  3. R. Bhagwan, K. Tati, Y.-C. Cheng, S. Savage, and G. M. Voelker. 2004. Total recall: System support for automated availability management. In Symposium on Networked Systems Design and Implementation, Vol. 1. Google ScholarGoogle ScholarDigital LibraryDigital Library
  4. J. Bloemer, M. Kalfane, M. Karpinski, R. Karp, M. Luby, and D. Zuckerman. 1995. An XOR-based erasure-resilient coding scheme. ICSI Technical Report, Article TR-95-048.Google ScholarGoogle Scholar
  5. B. Calder, J. Wang, A. Ogus, N. Nilakantan, A. Skjolsvold, S. McKelvie, Y. Xu, S. Srivastav, J. Wu, H. Simitci, J. Haridas, C. Uddaraju, H. Khatri, A. Edwards, V. Bedekar, S. Mainali, R. Abbasi, A. Agarwal, M. Fahim ul Haq, M. Ikram ul Haq, D. Bhardwaj, S. Dayanand, A. Adusumilli, M. McNett, S. Sankaran, K. Manivannan, and L. Rigas. 2011. Windows azure storage: A highly available cloud storage service with strong consistency. In Symposium on Operating System Principles. Google ScholarGoogle ScholarDigital LibraryDigital Library
  6. Y. Chen, R. Griffith, D. Zats, A. D. Joseph, and R. Katz. 2012. Understanding TCP incast and its implications for big data workloads. University of California at Berkeley, Technical Report.Google ScholarGoogle Scholar
  7. Y. L. Chen, S. Mu, J. Li, C. Huang, J. Li, A. Ogus, and D. Phillips. 2017. Giza: Erasure coding objects across global data centers. In USENIX Annual Technical Conference. Google ScholarGoogle ScholarDigital LibraryDigital Library
  8. A. Cidon, S. M. Rumble, R. Stutsman, S. Katti, J. Ousterhout, and M. Rosenblum. 2013. Copysets: Reducing the frequency of data loss in cloud storage. In USENIX Annual Technical Conference. Google ScholarGoogle ScholarDigital LibraryDigital Library
  9. J. Cowling. 2016. Dropbox's Exabyte Storage System. Retrieved from https://code.facebook.com/posts/253562281667886/data-scale-june-2016-recap/.Google ScholarGoogle Scholar
  10. A. Dimakis. 2016. Online Wiki Bibliography for Distributed Storage Papers. Retrieved from http://storagewiki.ece.utexas.edu/.Google ScholarGoogle Scholar
  11. A. Dimakis, P. Godfrey, Y. Wu, M. Wainwright, and K. Ramchandran. 2007. Network coding for distributed storage systems. In IEEE Infocom. Google ScholarGoogle ScholarDigital LibraryDigital Library
  12. A. Dimakis, P. Godfrey, Y. Wu, M. Wainwright, and K. Ramchandran. 2010. Network coding for distributed storage systems. IEEE Transactions on Information Theory 56, 9 (Sept. 2010), 4539--4551. Google ScholarGoogle ScholarDigital LibraryDigital Library
  13. Sage A. Weil, Scott A. Brandt, Ethan L. Miller, Darrell D. E. Long, and Carlos Maltzahn. 2006. A scalable, high-performance distributed file system. In Proceedings of the 7th Symposium on Operating Systems Design and Implementation (OSDI'06). USENIX Association, Berkeley, CA, 307--320. Google ScholarGoogle ScholarDigital LibraryDigital Library
  14. D. Ford, F. Labelle, F. Popovici, M. Stokely, V. Truong, L. Barroso, C. Grimes, and S. Quinlan. 2010. Availability in globally distributed storage systems. In USENIX Symposium on Operating Systems Designs and Implementation, 1--7. Google ScholarGoogle ScholarDigital LibraryDigital Library
  15. Google. 2018. Snappy: A fast Compressor/Decompressor. Retrieved October 9, 2018, from https://google.github.io/snappy/.Google ScholarGoogle Scholar
  16. P. Gopalan, C. Huang, H. Simitci, and S. Yekhanin. 2012. On the locality of codeword symbols. IEEE Transactions on Information Theory 58, 11 (Nov. 2012), 6925--6934. Google ScholarGoogle ScholarDigital LibraryDigital Library
  17. C. Huang, H. Simitci, Y. Xu, A. Ogus, B. Calder, P. Gopalan, J. Li, and S. Yekhanin. 2012. Erasure coding in windows azure storage. In USENIX Annual Technical Conference. Google ScholarGoogle ScholarDigital LibraryDigital Library
  18. G. Joshi, Y. Liu, and E. Soljanin. 2012. Coding for fast content download. In Proceedings of the 50th Allerton Conference on Communication, Control, and Computing (Allerton) (Oct. 2012), 326--333.Google ScholarGoogle ScholarCross RefCross Ref
  19. J. Lacan, V. Roca, J. Peltotalo, and S. Peltotalo. 2009. Reed-Solomon Forward Error Correction (FEC) Schemes, RFC 5510. Retrieved on April 2009 from https://www.rfc-editor.org/info/rfc5510.Google ScholarGoogle Scholar
  20. R. Li, X. Li, P. P. C. Lee, and Q. Huang. 2017. Repair pipelining for erasure-coded storage. In USENIX Annual Technical Conference. Google ScholarGoogle ScholarDigital LibraryDigital Library
  21. M. Luby. 2016. Capacity bounds for distributed storage. arXiv article, April 2018, arXiv:1610.03541v5.Google ScholarGoogle Scholar
  22. M. Luby, A. Shokrollahi, M. Watson, T. Stockhammer, and L. Minder. 2011. RaptorQ Forward Error Correction Scheme for Object Delivery, RFC 6330. Retrieved on August 2011 from https://www.rfc-editor.org/info/rfc6330.Google ScholarGoogle Scholar
  23. S. Muralidhar, W. Lloyd, S. Roy, C. Hill, E. Lin, W. Liu, S. Pan, S. Shankar, V. Sivakumar, L. Tang, and S. Kumar. 2014. Facebook’s warm BLOB storage system. USENIX Conference on Operating Systems Design and Implementation 11 (2014), 383--398. Google ScholarGoogle ScholarDigital LibraryDigital Library
  24. K. V. Rashmi, P. Nakkiran, J. Wang, N. B. Shah, and K. Ramchandran. 2015. Having your cake and eating it too: Jointly optimal erasure codes for I/O, storage, and network-bandwidth. In 13th USENIX File and Storage Technologies (File and Storage Technologies (FAST’15)), Vol. 13. USENIX Association. Google ScholarGoogle ScholarDigital LibraryDigital Library
  25. K. V. Rashmi, N. B. Shah, D. Gu, H. Kuang, D. Borthakur, and K. Ramchandran. 2014. A “Hitchhiker’s” guide to fast and efficient data reconstruction in erasure-coded data centers. In ACM Conference on SIGCOMM. Google ScholarGoogle ScholarDigital LibraryDigital Library
  26. R. Recio, B. Metzler, P. Culley, J. Hilland, and D. Garcia. 2007. A Remote Direct Memory Access Protocol Specification, RFC 5040. Retrieved October 2007 from https://www.rfc-editor.org/info/rfc5040.Google ScholarGoogle Scholar
  27. L. Rizzo. 1997. Effective erasure codes for reliable computer communication protocols. ACM SIGCOMM Computer Communication Review 27, 2 (April 1997), 24--36. Google ScholarGoogle ScholarDigital LibraryDigital Library
  28. R. Rodrigues and B. Liskov. 2005. High availability in DHTs: Erasure coding vs. replication. Peer-to-Peer Systems IV (2005), 226--239. Google ScholarGoogle ScholarDigital LibraryDigital Library
  29. Samsung. 2016. SM863a Specification Sheet. Retrieved October 8, 2018, from http://www.samsung.com/semiconductor/minisite/ssd/product/enterprise/sm863a.html.Google ScholarGoogle Scholar
  30. M. Sathiamoorty, M. Asteris, D. Papailiopoulos, A. Dimakis, R. Vadali, S. Chen, and D. Borthakur. 2013. XORing elephants: Novel erasure codes for big data. Proceedings of the VLDB Endowment 6, 5 (2013), 325--336. Google ScholarGoogle ScholarDigital LibraryDigital Library
  31. A. Shokrollahi and M. Luby. 2011. Raptor codes. Foundations and Trends in Communications and Information Theory 6, 3--4 (2011), 213--322. Google ScholarGoogle ScholarDigital LibraryDigital Library
  32. M. Silberstein, L. Ganesh, Y. Wang, and M. Dahlin L. Alvisi. 2014. Lazy means smart: Reducing repair bandwidth costs in erasure-coded distributed storage. In International Conference on Systems and Storage, 1--7. Google ScholarGoogle ScholarDigital LibraryDigital Library
  33. E. Sit, A. Haeberlen, F. Dabek, B. Chun, H. Weatherspoon, R. Morris, M. Kaashoek, and J. Kubiatowicz. 2006. Proactive replication for data durability. International Workshop on Peer-to-Peer Systems 5 (2006). http://iptps06.cs.ucsb.edu/papers/Sit-tempo.pdf.Google ScholarGoogle Scholar
  34. H. Weatherspoon and J. Kubiatowicz. 2002. Erasure coding vs. replication: A quantitative comparison. In Proceedings of the First International Workshop on Peer-to-Peer Systems (2002). 328--337. Google ScholarGoogle ScholarDigital LibraryDigital Library

Index Terms

  1. Liquid Cloud Storage

                Recommendations

                Comments

                Login options

                Check if you have access through your login credentials or your institution to get full access on this article.

                Sign in

                Full Access

                PDF Format

                View or Download as a PDF file.

                PDF

                eReader

                View online with eReader.

                eReader

                HTML Format

                View this article in HTML Format .

                View HTML Format