基于动态混合分片机制的云存储系统I/O性能优化算法

为了解决云存储系统中存储I/O性能优化的问题,从数据分布的角度进行研究,建立了存储分片性能模型,并以此为基础给出了以I/O性能最优为目标的分片决策算法以及最优分片数的计算方法,提出了基于动态混合分片机制的数据分布算法(DADH).DHAH能充分考虑各存储节点和客户机的实际性能,动态地进行文件分片决策和最优分片数的计算,并且能根据存储节点的实际性能优化数据分布.实验结果表明:DADH较传统数据分布算法具有最优的I/O性能,并且算法性能提升效果在各种带宽下平均达到18%～44%.

Optimizing Algorithm for I/O Performance of Cloud Storage System Based on Dynamic Hybrid Range Mechanism

In the aim of optimizing the I/O performance in the cloud storage system,the partitioning decision algorithm and the computational method of the best partitioning number based on the partitioning storage model of I/O performance were put forward.An algorithm called DADH was proposed to make partitioning decision and compute the best number of partitioning dynamically by taking each storage node's and client's actual performance into account,and to optimize data distribution according to the storage nodes' actual performance.Results show that DADH has the higher I/O performance than traditional data distribution algorithm with the advance from 18% to 44%.