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236601 - Coding and Algorithms for Memories Lecture 14 1.

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Presentation on theme: "236601 - Coding and Algorithms for Memories Lecture 14 1."— Presentation transcript:

1 236601 - Coding and Algorithms for Memories Lecture 14 1

2 Large Scale Storage Systems 2 Big Data Players: Facebook, Amazon, Google, Yahoo,… Cluster of machines running Hadoop at Yahoo! (Source: Yahoo!) Failures are the norm

3 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 3

4 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 – Solution: Locally Recoverable Codes (LRC) 4

5 Locally Recoverable Codes (LRC) 5 1 1 2 2 k k k+1 n n 1 2 r r-1 2 2

6 Locally Recoverable Codes (LRC) A Locally Recoverable Code (n,k,r) is a code of length n, dimension k, and locality r The problem: Given n,k,r what is the best minimum distance d of the code? A code achieving the maximum d is called an optimal LRC code 6


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