With the rapid increase in data storage, distributed storage systems need to add new nodes to reduce storage and computation pressure. However, the existing scaling schemes are not efficient enough. To solve these issues, A scaling scheme called S-MBR is proposed, which has two notable features. Firstly, S-MBR achieves uniform data distribution by using a symmetric data layout to evenly place data blocks on the expanded data nodes. Secondly, S-MBR minimizes data movement during data redistribution and parity update processes by re-locating data blocks to symmetric positions and performing partial parity block calculations after the storage nodes are expanded. S-MBR can reach the lower bound of data migration volume when the number of nodes required for reconstruction is equal to the number of nodes required for repair. According to mathematical analysis and experimental results, compared to many typical scaling schemes, S-MBR can reduce data transmission volume by up to approximately 93% and average response time by around 41%.
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