Repairable Fountain Codes

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Repairable Fountain Codes

Megasthenis Asteris, Alexandros G. Dimakis

IEEE JOURNAL ON SELECTED AREAS IN COMMUNICATIONS, VOL. 32, NO. 5, MAY 2014

2014/5/22

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Outline

• Introduction• Problem Description• Prior Work• Repairable Fountain Codes• A Graph Perspective• Simulations• Conclusion

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Introduction

• Fountain codes [2], [3], [4] form a new family of linear erasure codes with several attractive properties.

• For a given set of k input symbols, produces a potentially limitless stream of output symbols.

• Randomly selected subset of (1+ ɛ)k encoded symbols, a decoder should be able to recover the original k input symbols with high probability (w.h.p.) for some small overhead ɛ.

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[2] J. W. Byers, M. Luby, M. Mitzenmacher, and A. Rege, “A digital fountain approach to reliable distribution of bulk data,” [3] M. Luby, “LT codes,” [4] A. Shokrollahi, “Raptor codes,”

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Introduction

• This paper design a new family of Fountain codes that combine multiple properties appealing to distributed storage.– systematic form– efficient repair– locality

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Introduction

• A key observation is that in a systematic linear code, locality is strongly connected to the sparsity of parity symbols [11]– i.e., the maximum number of input symbols

combined in a parity symbol.• A parity symbol along with the systematic

symbols covered by it form a local group.– The smaller the size of the local group, the lower

the locality of the symbols in it.

2014/5/22[11] P. Gopalan, C. Huang, H. Simitci, and S. Yekhanin, “On the locality of codeword symbols,”

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Introduction

• The availability of a systematic symbol naturally extends the notion of locality, measuring the number of disjoint local groups the symbol belongs to.– define the availability of a systematic symbol as

the number of disjoint sets of encoded symbols which can be used to reconstruct that particular symbol.

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Outline

• Introduction• Problem Description• Prior Work• Repairable Fountain Codes• A Graph Perspective• Simulations• Conclusion

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Problem Description

• Given k input symbols, elements of a finite field , we want to encode them into n symbols using a linear code.

• An input vector u ∈ produces a codeword v = uG ∈.– by a k × n generator matrix G.

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Problem Description

• G to have the following properties:– Systematic form– Rateless property– MDS property– Low locality– High Availability

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Problem Description

• For any code, any sufficiently large subset of encoded symbols should allow recovery of the original data.– In the case of MDS codes, an information

theoretically minimum subset of k encoded symbols suffices to decode.

– When equipped with systematic form, the generator matrix of an MDS code affords no zero coefficient in the parity generating columns.

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Problem Description

• This paper require that for ɛ > 0, a set of k’= (1+ ɛ)k randomly selected encoded symbols suffice to decode with high probability.– This property called as near-MDS.

• It is impossible to recover the original message with high probability if the parities are linear combinations of fewer than Ω(log k) input symbols.

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Outline

• Introduction• Problem Description• Prior Work• Repairable Fountain Codes• A Graph Perspective• Simulations• Conclusion

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Prior Work

• In LT codes, the average degree of the output symbols, i.e., the number of input symbols combined into an output symbol, is O (log k).

• However, that sparsity in this case does not imply good locality, since LT codes lack systematic form.

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Prior Work

• Raptor codes, a different class of Fountain codes.– The core idea is to precode the input symbols

prior to the application of an appropriate LT code.– The k input symbols can be retrieved in linear

time by a set of (1 + ɛ)k encoded symbols• However, the original Raptor design does not

feature the highly desirable systematic form.– does not imply good locality.

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Prior Work

• [4], Shokrollahi provided a construction that yields a systematic flavor of Raptor codes.– Encoding is not applied directly on the input

symbols.– Complexity O().

• The source symbols appear in the encoded stream– But the parity symbols are no longer sparse in the

original input symbols,

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[4] A. Shokrollahi, “Raptor codes,”

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Prior Work

• [16], Gummadi proposes systematic variants of LT and Raptor codes that feature low (even constant) expected repair complexity.– However, the overhead ɛ required for decoding is

suboptimal: it cannot be made arbitrarily small.

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[16] R. Gummadi, “Coding and scheduling in networks for erasures and broadcast,”

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Outline

• Introduction• Problem Description• Prior Work• Repairable Fountain Codes• A Graph Perspective• Simulations• Conclusion

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Repairable Fountain Codes

• A new family of Fountain codes that are systematic and also have sparse parities.– Each parity symbol is a random linear combination

of up to d = Ω(logk) randomly chosen input symbols.

– Require that a set of k’= (1+ ɛ)k randomly selected encoded symbols

– with probability of failure vanishing like 1/poly(k).

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Repairable Fountain Codes

• Given a vector u of k input symbols in • The code is a linear mapping of u to a vector v

of higher dimension n through a k × n matrix G.

• A single parity symbol is constructed in a two step process.

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Repairable Fountain Codes

• The parity is the linear combination of the symbols selected in the first step, weighted with the coefficients drawn in the second step.– First, d input symbols are successively selected

uniformly at random, independently, with replacement.

– Then, a coefficient is uniformly drawn from

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Repairable Fountain Codes

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U is the set of k input symbols

V is the set of nencoded symbols.

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Repairable Fountain Codes

• Returning to the matrix representation• Generator matrices G = [ | P ].• Every encoded symbol corresponds to a

column of G.• P, the part responsible for the construction of

the parity symbols– a random matrix whose columns are sparse, each

bearing at most d(k) nonzero entries.

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Repairable Fountain Codes

• In summary, decreasing d(k) improves the locality of the code– How small d(k) can be to ensure that randomly

selected set of k’= (1+ɛ)k symbols is decodable.

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Repairable Fountain Codes

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Repairable Fountain Codes

• From the two theorems, it follows that our codes achieve optimal locality with a logarithmic degree for every parity symbol.– Original data is reconstructed in O() using

Maximum Likelihood (ML) decoding– Wiedemann algorithm can reduce complexity to

O(polylog(k)) on average.

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Repairable Fountain Codes

• Theorem 3. Let rk be the total number of parities generated, with each parity symbol constructed as a linear combination of d(k) = c log(k) independently selected symbols uniformly at random with replacement.

• The expected number of parities covering a systematic symbol u is

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Repairable Fountain Codes

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Availability

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Outline

• Introduction• Problem Description• Prior Work• Repairable Fountain Codes• A Graph Perspective• Simulations• Conclusion

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A Graph Perspective

• First, consider a balanced random bipartite graph where |U| = |V | = k and each vertex of V is randomly connected to d(k) = c log k nodes in U.

• A classical result by Erd˝os and Renyi [14] shows that these graphs will have perfect matchings with high probability.

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[14] P. Erd˝os and A. R´enyi, “On random matrices,”

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A Graph Perspective• However, the graphs are unbalanced– |U| = k and |V | = k’ = (1+ ɛ)k vertices, for ɛ ≥ 0

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A Graph Perspective

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Outline

• Introduction• Problem Description• Prior Work• Repairable Fountain Codes• A Graph Perspective• Simulations• Conclusion

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Simulations

• This paper experimentally evaluate the probability that decoding fails when a randomly selected subset of encoded symbols is available at the decoder.– set the rate equal to ½.– the generator matrix comprises the k columns of

the identity matrix and k parity generating columns.

– d(k) = c log(k).

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Simulations

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d(k) = c log(k), with c = 6.

Expected decodingoverhead ɛ = 1− 2Pe.

k = 100,300,500.

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Simulations

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d(k) = c log(k), with c = 4.

k = 100,300,500.

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Outline

• Introduction• Problem Description• Prior Work• Repairable Fountain Codes• A Graph Perspective• Simulations• Conclusion

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Conclusion

• This paper introduce a new family of Fountain codes that are systematic and also have parity symbols with logarithmic sparsity.

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Conclusion

• Main technical contribution is a novel random matrix result: a family of random matrices with non independent entries have full rank with high probability.– The analysis builds on the connections of matrix

determinants to flows on random bipartite graphs, using techniques from [14], [15].

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[14] P. Erd˝os and A. R´enyi, “On random matrices,”[15] A. G. Dimakis, V. Prabhakaran, and K. Ramchandran, “Decentralized erasure codes for distributed networked storage,”

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Conclusion

• One disadvantage of this construction is higher decoding complexity O().– Wiedemann’s algorithm [13] can be used to

decode in O(polylogk) time.– O(k log k) for LT and O(k) for Raptor but offer no

locality.

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[13] D. Wiedemann, “Solving sparse linear equations over finite fields,”