LT Codes For Efficient and Reliable Distributed Storage Systems Revisited

TL;DR

This paper analyzes the use of LT codes in distributed storage, introducing new code classes and theoretical insights to improve efficiency and reliability, especially for small data fragments.

Contribution

It introduces flat and array BP-XOR codes as deterministic variants of LT codes, establishing their equivalence with edge-colored graph models for better design and analysis.

Findings

Array BP-XOR codes can be designed using graph theory.

Edge-colored graph models are equivalent to array BP-XOR codes.

Theoretical analysis supports efficient LT code application in storage systems.

Abstract

LT codes and digital fountain techniques have received significant attention from both academics and industry in the past few years. There have also been extensive interests in applying LT code techniques to distributed storage systems such as cloud data storage in recent years. However, Plank and Thomason's experimental results show that LDPC code performs well only asymptotically when the number of data fragments increases and it has the worst performance for small number of data fragments (e.g., less than 100). In their INFOCOM 2012 paper, Cao, Yu, Yang, Lou, and Hou proposed to use exhaustive search approach to find a deterministic LT code that could be used to decode the original data content correctly in distributed storage systems. However, by Plank and Thomason's experimental results, it is not clear whether the exhaustive search approach will work efficiently or even correctly. This paper carries out the theoretical analysis on the feasibility and performance issues for applying LT codes to distributed storage systems. By employing the underlying ideas of efficient Belief Propagation (BP) decoding process in LT codes, this paper introduces two classes of codes called flat BP-XOR codes and array BP-XOR codes (which can be considered as a deterministic version of LT codes). We will show the equivalence between the edge-colored graph model and degree-one-and-two encoding symbols based array BP-XOR codes. Using this equivalence result, we are able to design general array BP-XOR codes using graph based results. Similarly, based on this equivalence result, we are able to get new results for edge-colored graph models using results from array BP-XOR codes.