一种云存储环境下的资源调度改进算法

如何将用户的海量数据以最小的耗时存储到数据中心,是提高云存储效益,解决其发展瓶颈所需考虑的关键问题本文首先证明了云存储环境下资源调度方案的存储最小耗时问题属于一个NPC问题,再针对现有算法对存储调度因素考虑不全面、调度结果易陷入局部最优等问题,提出了一种全新的资源调度算法,该算法首先利用三角模糊数层次分析法全面分析调度影响因素,得到存储节点的判断矩阵,用于构造后续的遗传算法目标函数,再将简单遗传算法从解的编码、交叉变异操作及致死染色体自我改善等角度进行创新,使其适用于云存储环境下的大规模资源调度,最后与OpenStack中的Cinder块存储算法及现有改进算法进行了分析比对,实验结果验证了本文所提算法的有效性,实现了更加高效的资源调度。

An improved resource scheduling algorithm in cloud storage environment

How to store the user"s massive data into the data center with the minimum time-consuming is the key issue to be considered in improving cloud storage efficiency and solving the bottleneck of its development. This paper first proved that the minimum storage time-consuming of resource scheduling scheme in cloud storage environment belongs to NPC problem. In view of the incomplete consideration of the existing scheduling algorithms and the problem that the scheduling result tends to fall into the local optimum, a new resource scheduling algorithm was proposed. The algorithm firstly used the triangular fuzzy analytic hierarchy process method to comprehensively analyze the scheduling effectting factors, the judgment matrix of storage nodes was obtained, which was used to construct the follow-up objective function of genetic algorithm, and then the simple genetic algorithm was innovated from the perspective of encoding, cross-mutation operation and self-improvement of lethal chromosome so that it is suitable for cloud storage environment. Finally, this paper analyzed and compared the Cinder block storage algorithm in OpenStack and the existing improved algorithms. The experimental results verified the effectiveness of the proposed algorithm and achieved more efficient resource scheduling.