基于稀疏随机矩阵的再生码构造方法

针对已有的再生码编码方案的运算是基于有限域GF（q）、运算复杂度高、效率低的问题，提出了一种将GF（2）上的稀疏随机矩阵和乘积矩阵框架相结合的再生码构造方法。首先，将文件数据矩阵式排布后根据编码矩阵进行行异或运算；其次，节点失效后，参与帮助节点根据失效节点的编码向量编码本地数据并发送至修复节点；最后，修复节点根据接收到的数据译码出失效节点原有的数据。实验结果表明修复带宽至多只有传统纠删码修复方案的1/10，相比基于传统范德蒙编码矩阵的再生码，编码速率提升了70%，译码恢复速率提升了50%，方便了再生码在大规模存储系统中的应用。

Regenerating codes construction method based on sparse random matrix

Concerning the problem that the calculations of the existing regenerating code schemes is based on GF(q), and it has high computational complexity and low efficiency, a regenerating code construction method based on sparse random matrix over GF(2) and product matrix framework was proposed. Firstly, file data was arranged in a matrix and the row XOR operation was performed according to encoding matrix. Secondly, local data was encoded by helper nodes according to failed node's encoding vector and sent to repair node. Finally, the failed node's data was decoded by repair node according to received data. The experimental results show that the repair bandwidth of the proposed method is only one-tenth of traditional erasure code at most, and the encoding rate increases by 70% and decoding recovery rate increases by 50% compared with regenerating code based on conventional Vandermonde matrix, which facilitates the application of regenerating code in massive storage system.