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236601 - Coding and Algorithms for Memories Lecture 12 1.

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Presentation on theme: "236601 - Coding and Algorithms for Memories Lecture 12 1."— Presentation transcript:

1 Coding and Algorithms for Memories Lecture 12 1

2 Array Codes and Distributed Storage 2

3 Large Scale Storage Systems 3 Big Data Players: Facebook, Amazon, Google, Yahoo,… Cluster of machines running Hadoop at Yahoo! (Source: Yahoo!) Failures are the norm

4 Node failures at Facebook 4 Date XORing Elephants: Novel Erasure Codes for Big Data M. Sathiamoorthy, M. Asteris, D. Papailiopoulos, A. G. Dimakis, R. Vadali, S. Chen, and D. Borthakur, VLDB 2013

5 Problem Setup Disks are stored together in a group (rack) Disk failures should be supported Requirements: – Support as many disk failures as possible – And yet… Optimal and fast recovery Low complexity 5

6 Problem Setup Question 1: How many extra disks are required to support a single disk failure? Question 2: How many extra disks are required to support two disk failures? Question 3: How many extra disks are required to support d disk failures? 6 A A B B C C A+B+C A A B B C C  A+  B +  C A A B B C C A+B+C  A+  B +  C  ’A+  ’ B+  ’C {(x 1,x 2,x 3,x 4 ): x 1 +x 2 +x 3 +x 4 = 0 } {(x 1,x 2,x 3,x 4,x 5 ): x 1 +x 2 +x 3 +x 4 =0  x 1 +  x 2 +  x 3 +x 5 =0 } {(x 1,x 2,x 3,x 4,x 5,x 6 ): x 1 +x 2 +x 3 +x 4 =0  x 1 +  x 2 +  x 3 +x 5 =0  ’x 1 +  ’x 2 +  ’x 3 +x 6 =0} {(x 1,x 2,x 3,x 4 ): H 1 ∙(x 1,x 2,x 3,x 4 ) T =0} H 1 = (1,1,1,1) {(x 1,x 2,x 3,x 4,x 5 ): H 2 ∙(x 1,x 2,x 3,x 4,x 5 ) T =0} H 2 = (1,1,1,1,0; , , ,0,1) {(x 1,x 2,x 3,x 4,x 5,x 6 ):H 3 ∙(x 1,x 2,x 3,x 4,x 5,x 6 ) T =0} H 3 = (1,1,1,1,0,0; , , ,0,1,0;  ’,  ’,  ’,0,1,0)

7 Reed Solomon Codes 7

8 Advantages: – Support the maximum number of disk failures – Are very comment in practice and have relatively efficient encoding/decoding schemes Disadvantages – Require to work over large fields – Need to read all the disks in order to recover even a single disk failure – not efficient rebuild 8

9 Reed Solomon Codes Advantages: – Support the maximum number of disk failures – Are very comment in practice and have relatively efficient encoding/decoding schemes Disadvantages – Require to work over large fields Solution: EvenOdd Codes – Need to read all the disks in order to recover even a single disk failure – not efficient rebuild Solution: ZigZag Codes 9

10 EVENODD Codes Designed by Mario Balum, Jim Brady, Jehoshua Bruck, and Jai Menon Goal: Construct array codes correcting 2 disk failures using only binary XOR operations – No need for calculations over extension fields Code construction: – Every disk is a column – The array size is (m-1)x(m+2), m is prime – The last two arrays are used for parity 10

11 EVENODD Codes

12 The Repair Problem P1 P3 P4 P2 A disk is lost – Repair job starts Access, read, and transmit data of disks! Overuse of system resources during single repair Goal: Reduce repair cost in a single disk repair Facebook’s storage Scheme: – 10 data blocks – 4 parity blocks – Can tolerate any four disk failures RS code

13 ZigZag Codes Designed by Itzhak Tamo, Zhiying Wang, and Jehoshua Bruck The goal: construct codes correcting the max number of erasures and yet allow efficient reconstruction if only a single drive fails 13

14 ZigZag Codes Example 14 aba+ba+2d cdc+dc+b

15 ZigZag Codes Lower bound: The min amount of data required to be read to recover a single drive failure – (n,k) code: n drives, k information, and n-k redundancy – M- size of a single drive in bits For (n,n-2) code it is required to read at least 1/2 from the remaining drives, that is at least (1/2)(n-1)M bits – The last example is optimal In general, for (n,n-r) code it required to read at least 1/r from the remaining drives (1/r)(n-1)M 15

16 ZigZag Codes Example 16 info 1info 2info 3 Row parity ZigZag parity

17 ZigZag Codes Example 17 info 1info 2info 3 Row parity ZigZag parity


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