The storage demand for large-scale data in modern society is increasing, and the traditional storage architecture has limitations in disaster tolerance, scalability, and maintenance management, resulting in additional costs for enterprises. In order to solve this problem, the distributed system(DS) and data storage technology can be fully combined to give full play to the advantages of the DS in processing massive data. However, DSs are faced with serious data storage overhead and high availability problems. For these two problems, this paper designs a corresponding optimization scheme and verifies the effectiveness of the optimization scheme on the performance of DSs. The results show that the local repair code based on the system MSR code is conducive to the rapid data repair, and is consistent with the data recovery of the simulated annealing algorithm, saving the time overhead of the data reading process and data repair process; and the optimized high availability scheme can Reduce system failure recovery time.

Sahiilin Kavitane. Distributed System Optimization Relying on Simulated Annealing Algorithm and Data Storage Technology. Distributed Processing System (2021), Vol. 2, Issue 4: 61-68. https://doi.org/10.38007/DPS.2021.020408.

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