Successfully reported this slideshow.

More Related Content

Related Books

Free with a 14 day trial from Scribd

See all

Simple regenerating codes: Network Coding for Cloud Storage

  1. 1. Simple Regenerating Codes: Network Coding for Cloud Storage Dimitris S. Papailiopoulos, Jianqiang Luo, Alexandros G. Dimakis, Cheng Huang, and Jin Li INFOCOM 2012 Presented by Tangkai
  2. 2. Index  About the author  Introduction  SRC  Simulations  Conclusion
  3. 3. About the author  Jianqiang Luo ◦ Experience  Senior Software Engineer @ EMC  Received PhD, Wayne State University  Intern @ Microsoft, Data Domain  Team Leader @ Actuate  Received MS, SJTU ◦ Specialties  Working on distributed storage systems during PhD Performance profiling.
  4. 4. About the author  Alexandros G. Dimakis ◦ Assistant Professor Dept of EE – Systems, USC ◦ Research interests:  Communications, signal processing and networking. ◦ INFOCOM 2012 - 2 ◦ Erasure code MDS MSR MBR etc
  5. 5. About the author  Cheng Huang ◦ Education  Microsoft Research  Ph.D. Washington University  B.S. and M.S. EE Dept, SJTU ◦ Research interest  cloud services, internet measurements, erasure correction codes, distributed storage systems, peer-to- peer streaming, networking and multimedia communications. ◦ INFOCOM 2011  Public DNS System and Global Traffic Management  Estimating the Performance of Hypothetical Cloud Service Deployments: A Measurement-Based Approach
  6. 6. About the author  Jin Li ◦ Experience  Microsoft Research  BS/MS/PhD THU (within 7 years)  计算机普及要从娃娃抓起 ◦ Title  IEEE Fellow  GLOBECOM/ICME/ACM MM Chair
  7. 7. Index  About the author  Introduction  SRC  Simulations  Conclusion
  8. 8. Introduction  Background ◦ We have come into BIG DATA ERA!  Digital Universe 1.8 ZB (=1.8e9 TB)  Several PBs photo stored on Facebook  14.1PB data stored on Taobao (2010) ◦ Data security is IMPORTANT  Free from unwanted actions of unauthorized users.  Free from data loss caused by destructive forces
  9. 9. Introduction  Background ◦ Recovery  rare exception -> regular operation  GFS[1]:  Hundreds or even thousands of machines  Inexpensive commodity parts  High concurrency/IO ◦ High failure tolerance, both for  High availability and to prevent data loss [1] S. Ghemawat, H. Gobioff, and S.-T. Leung, “The Google file system,” in SOSP ’03: Proc. of the 19th ACM Symposium on Operating Systems Principles, 2003.
  10. 10. Introduction  Background ◦ Erasure coding > replication  1. redundancy level, reliability  2. reliability, storage cost ◦ Some applications  Cloud storage systems  Archival storage  Peer-to-peer storage systems
  11. 11. Introduction  Erasure coding: MDS n=3 n=4 k=2 File or A A data A object A B B B B A+B A+B (3,2) MDS code, (single parity) A+2B used in RAID 5 (4,2) MDS code. Tolerates any 2 failures Used in RAID 6
  12. 12. Introduction  Erasure coding vs. Replica[3]erasure code (4,2) MDS Replication (any 2 suffice to recover) File or A A data A object A B vs B B A+B B A+2B [3]A. G. Dimakis, P. G. Godfrey, Y. Wu, M. J. Wainwright, and K. Ramchandran,“Network coding for distributed storage systems,” in IEEE Trans. on Inform. Theory, vol. 56, pp.
  13. 13. Introduction  Erasure coding vs. Replica[3]erasure code (4,2) MDS Replication (any 2 suffice to recover) File or A A data A object A B Erasure coding is introducing redundancy in an optimal way. vs B Very useful in practice i.e. Reed-Solomon codes, Fountain Codes, (LT and Raptor)… B A+B B A+2B [3]A. G. Dimakis, P. G. Godfrey, Y. Wu, M. J. Wainwright, and K. Ramchandran,“Network coding for distributed storage systems,” in IEEE Trans. on Inform. Theory, vol. 56, pp.
  14. 14. Introduction  Metrics ◦ Storage per node (α) ◦ Repair Bandwidth per single node repair (γ) ◦ Disk Accesses per single node repair (d) ◦ Effective Coding Rate (R)  Contribution ◦ High R, Small d ◦ Low repair computation complexity
  15. 15. Index  About the author  Introduction  SRC  Simulations  Conclusion
  16. 16. SRC  SRC: Simple Regenerating Codes ◦ Regenerating Codes  address the issue of rebuilding (also called repairing) lost encoded fragments from existing encoded fragments. This issue arises in distributed storage systems where communication to maintain encoded redundancy is a problem.
  17. 17. SRC  Object  Requirement I: (n, k) property  MDS[2] [2] Alexandros G. Dimakis, Kannan Ramchandran, Yunnan Wu, Changho Suh: A Survey on Network Codes for Distributed Storage. in Proceedings of the
  18. 18. SRC ◦ MDS
  19. 19. SRC  Requirement II: efficient exact repair ◦ Efficient: Low complexity ◦ Exact repair (vs. functional repair)[3] :  1. [demands]Data have to stay in systematic form  2. [complexity]Updating repairing-decoding rules-> additional overhead  3. [security] dynamic repairing-and-decoding rules observed by eavesdroppers -> information leakage [2] Changho Suh, Kannan Ramchandran: Exact Regeneration Codes for Distributed Storage Repair Using Interference Alignment. in IEEE TRANSACTIONS ON INFORMATION THEORY, VOL. 57, NO. 3, MARCH
  20. 20. SRC  Solution ◦ MDS codes are used to provide reliability to meets Requirement I ◦ simple XORs applied over the MDS coded packets provide efficient exact repair to meets Requirement II
  21. 21. SRC  Construction
  22. 22. SRC  Repair
  23. 23. (n,k,2)-SRC  Code Construction ◦ File f , of size M = 2k ◦ Split into 2 parts ◦ 1. 2 independent (n,k)-MDS encoding ◦ 2. Generating a parity sum vector using XOR
  24. 24. (n,k,2)-SRC  Distribution ◦ 3n chunks in n storage nodes
  25. 25. (n,k,2)-SRC  Repair
  26. 26. (n,k,f)-SRC  General Code Construction ◦ File f , of size M = fk ◦ Cut into f parts ◦ 1. f independent (n,k)-MDS encoding ◦ 2. Generating a parity sum vector using XOR
  27. 27. (n,k,f)-SRC  Distribution ◦ (f+1)n chunks in n storage nodes
  28. 28. (n,k,f)-SRC  Repair
  29. 29. (n,k,f)-SRC  Theorem ◦ Effective Coding Rate (R)  SRC is a fraction f/f+1 of the coding rate of an (n, k) MDS code, hence is upper bounded
  30. 30. (n,k,f)-SRC  Theorem ◦ Effective Coding Rate (R)
  31. 31. (n,k,f)-SRC  Theorem ◦ Storage per node (α) ◦ Repair Bandwidth per single node repair (γ) ◦ Disk Accesses per single node repair (d)  Seek time
  32. 32. (n,k,f)-SRC  Theorem ◦ Disk Accesses per single node repair (d)  Starting with f disk accesses for the first chunk repair
  33. 33. (n,k,f)-SRC  Theorem ◦ Disk Accesses per single node repair (d)  each additional chunk repair requires an additional disk access
  34. 34. (n,k,f)-SRC  Comparasion
  35. 35. (n,k,f)-SRC  Asymptotics of the SRC -> MDS ◦ let the degree of parities f grow as a function of k ◦ Repair Bandwidth per single node repair (γ) ◦ Effective Coding Rate (R)
  36. 36. Index  About the author  Introduction  SRC  Simulations  Conclusion
  37. 37. Simulations  Simulator Introduction ◦ One master, other storage server. ◦ Chunks form the smallest accessible data units and in our system are set to be 64MB  Simulator Validation ◦ 16 machines ◦ 1Gbps network. ◦ 410GB data per machine ◦ Approximately 6400 chunks
  38. 38. Simulations  Simulator Validation ◦ matches very well, when the percentile is below 95
  39. 39. Simulations  Storage Cost Analysis ◦ 3-way replication as baseline
  40. 40. Simulations  Repair Performance ◦ Calculated on time ◦ Highlights: Scalability
  41. 41. Simulations  Degraded Read Performance ◦ The only difference is after a chunk is repaired, we do not write it back.
  42. 42. Simulations  Data Reliability Analysis ◦ simple Markov model to estimate the reliability ◦ 5 years /1PB data / ◦ 30 min for replica / 15 min for SRC
  43. 43. Simulations  Data Reliability Analysis  Several order of magnitude of reliablity  Scalability
  44. 44. Index  About the author  Introduction  SRC  Simulations  Conclusion
  45. 45. Conclusions  Highlight ◦ R-S  Low IO/bandwidth -> scalability ◦ replica  High reliability  Decent repair/degraded read performance
  46. 46. Critical Thinking  Simulation  (n, k)as n grows, erasure performance is weaker  Compare ◦ MSR? ◦ Exact? ◦ Implementation - > Simulation

×