Advertisement

Understanding the Behavior of Solid State Disk

  • Qingchao Cai
  • Rajesh Vellore Arumugam
  • Quanqing Xu
  • Bingsheng He
Part of the Proceedings in Adaptation, Learning and Optimization book series (PALO, volume 1)

Abstract

In this paper, we develop a family of methods to characterize the behavior of new-generation Solid State Disks (SSDs). We first study how writes are handled inside the SSD by varying request size of writes and detecting the placement of requested pages. We further examine how this SSD performs garbage collection and flushes write buffer. The result shows that the clustered pages must be written and erased simultaneously, otherwise significant storage waste will arise if such clustered pages are partially written.

We then conduct two case studies to analyze the storage efficiency when an SSD is used for server storage and the cache layer of a hybrid storage system. In the first case, we find that a moderate storage waste exists, whereas in the second case, the number of written pages caused by a write request can be as much as 4.2 times that of pages requested, implying an extremely low storage efficiency. We further demonstrate that most of such unnecessary writes can be avoided by simply delaying the issuance of internal write requests, which are generated when a read request cannot be serviced by the cache layer. We believe that this study is helpful to understand the SSD performance behavior for data-intensive applications in the big-data era.

Keywords

Storage Solid State Disk Hybrid storage system Algorithm 

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. 1.
    FIU Traces, http://iotta.snia.org/traces/390 (retrieved September 11, 2014)
  2. 2.
    Hard disk drive, http://en.wikipedia.org/wiki/Hard_disk_drive (retrieved September 11, 2014)
  3. 3.
  4. 4.
    Agrawal, N., Prabhakaran, V., Wobber, T., Davis, J.D., Manasse, M.S., Panigrahy, R.: Design tradeoffs for ssd performance. In: ATC, Boston, Massachussetts, USA, pp. 57–70 (2008)Google Scholar
  5. 5.
    Axboe, J.: fio, https://github.com/axboe/fio (retrieved September 11, 2014)
  6. 6.
    Chen, F., Lee, R., Zhang, X.: Essential roles of exploiting internal parallelism of flash memory based solid state drives in high-speed data processing. In: HPCA, San Antonio, Texas, USA, pp. 266–277 (2011)Google Scholar
  7. 7.
    Chen, F., Luo, T., Zhang, X.: Caftl: A content-aware flash translation layer enhancing the lifespan of flash memory based solid state drives. In: FAST, San Jose, California, USA (2011)Google Scholar
  8. 8.
    Dirik, C., Jacob, B.: The performance of pc solid-state disks (ssds) as a function of bandwidth, concurrency, device architecture, and system organization. In: ISCA, Austin, TX, USA, pp. 279–289 (2009)Google Scholar
  9. 9.
    Guerra, J., Pucha, H., Glider, J., Belluomini, W., Rangaswami, R.: Cost effective storage using extent based dynamic tiering. In: FAST, San Jose, CA, USA (2011)Google Scholar
  10. 10.
    Gupta, A., Kim, Y., Urgaonkar, B.: Dftl: A flash translation layer employing demand-based selective caching of page-level address mappings. In: ASPLOS XIV, Washington, DC, USA, pp. 229–240 (2009)Google Scholar
  11. 11.
    He, B., Yu, J.X., Zhou, A.C.: Improving update-intensive workloads on flash disks through exploiting multi-chip parallelism. IEEE Transactions on Parallel and Distributed Systems (2014)Google Scholar
  12. 12.
    Kim, J., Seo, S., Jung, D., Kim, J.S., Huh, J.: Parameter-aware i/o management for solid state disks (ssds). IEEE Transactions on Computers 61(5), 636–649 (2012)CrossRefMathSciNetGoogle Scholar
  13. 13.
    Koltsidas, I., Viglas, S.D.: Flashing up the storage layer. Proceedings of the VLDB Endowment 1(1), 514–525 (2008)CrossRefGoogle Scholar
  14. 14.
    Lee, S.W., Park, D.J., Chung, T.S., Lee, D.H., Park, S., Song, H.J.: A log buffer-based flash translation layer using fully-associative sector translation. ACM Transactions on Embedded Computing Systems 6(3), article No. 18 (2007)Google Scholar
  15. 15.
    Li, Y., He, B., Luo, Q., Yi, K.: Tree indexing on flash disks. In: ICDE, Shanghai, China, pp. 1303–1306 (2009)Google Scholar
  16. 16.
    Li, Y., He, B., Yang, R.J., Luo, Q., Yi, K.: Tree indexing on solid state drives. Proceedings of the VLDB Endowment 3(1-2), 1195–1206 (2010)CrossRefGoogle Scholar
  17. 17.
    Ma, D., Feng, J., Li, G.: Lazyftl: A page-level flash translation layer optimized for nand flash memory. In: SIGMOD, Athens, Greece (2011)Google Scholar
  18. 18.
    Min, C., Kim, K., Cho, H., Lee, S.W., Eom, Y.I.: Sfs: Random write considered harmful in solid state drives. In: FAST, San Jose, CA, USA (2012)Google Scholar
  19. 19.
    Park, C., Seo, E., Shin, J.Y., Maeng, S., Lee, J.: Exploiting internal parallelism of flash-based ssds. Computer Architecture Letters 9(1), 9–12 (2010)CrossRefGoogle Scholar
  20. 20.
    Park, S., Shen, K.: Fios: a fair, efficient flash i/o scheduler. In: FAST, San Jose, CA, USA (2012)Google Scholar
  21. 21.
    Pritchett, T., Thottethodi, M.: Sievestore: A highly-selective, ensemble-level disk cache for cost-performance. In: ISCA, Saint-Malo, France, pp. 163–174 (2010)Google Scholar
  22. 22.
    Saxena, M., Swift, M.M., Zhang, Y.: Flashtier: A lightweight, consistent and durable storage cache. In: EuroSys, Bern, Switzerland, pp. 267–280 (2012)Google Scholar
  23. 23.
    Seol, J., Shim, H., Kim, J., Maeng, S.: A buffer replacement algorithm exploiting multi-chip parallelism in solid state disks. In: CASE, Grenoble, France, pp. 137–146 (2009)Google Scholar
  24. 24.
    Srinivasan, M.: Flashcache, https://github.com/facebook/flashcache (retrieved September 11, 2014)
  25. 25.
    Verma, A., Koller, R., Useche, L., Rangaswami, R.: Srcmap: energy proportional storage using dynamic consolidation. In: FAST, San Jose, California, USA (2010)Google Scholar

Copyright information

© Springer International Publishing Switzerland 2015

Authors and Affiliations

  • Qingchao Cai
    • 1
  • Rajesh Vellore Arumugam
    • 2
  • Quanqing Xu
    • 2
  • Bingsheng He
    • 1
  1. 1.School of Computer EngineeringNanyang Technological UniversitySingaporeSingapore
  2. 2.Data Storage InstituteA*STARSingaporeSingapore

Personalised recommendations