云存储系统中数据冗余策略优化问题

Research of Data Redundancy Policy Optimization Problem in Cloud Storage System

针对云存储系统现有冗余策略及其改进策略的不足,提出一种将完全备份与改进的RS(Reed-Solomon)纠删码结合的自适应数据冗余策略RIRS(Replication Improved RS)。该策略能根据具体应用环境调整参数以最大限度地优化系统性能。在参数调整使其退化成纯粹的完全备份方法条件下,提出一个动态副本管理优化模型DRMO(Dynamic Replication Management Optimized)对其进行改进。实现副本数和副本位置的动态调整。分析表明,RIRS充分融合了完全备份和纠删码冗余的优势,能节省存储空间,降低访问时延并提高系统的可靠性和稳定性。在此基础上提出的DRMO能在满足数据有效性要求的同时,进一步节省存储空间并实现负载均衡。

For the lack of existing redundancy strategy and the improved ones in cloud storage system, a adaptive data redundancy strategy RIRS （Replication Improved Reed-Solomon） is proposed, which combined full backup with improved RS （Reed-Solomon） erasure codes. The policy can adjust parameters according to the specific application environment to optimize system performance. When it degenerated into pure full backup method, proposed a dynamic replica management optimization model DRMO （Dynamic Replication Management Optimized） to improve it. The analysis showed that the strategy can meet the requirements of data validation, save storage space, achieve load balancing and improve the recoverability of the system at the same time.