基于Map-Reduce模型的云资源调度方法研究

Research on Cloud Resource Scheduling Method Based on Map-Reduce

为提高Map-Reduce模型资源调度问题的求解效能,分别考虑Map和Reduce阶段的调度过程,建立带服务质量(QoS)约束的多目标资源调度模型,并提出用于模型求解的混沌多目标粒子群算法。算法采用信息熵理论来维护非支配解集,以保持解的多样性和分布均匀性;在利用Sigma方法实现快速收敛的基础上,引入混沌扰动机制,以提高种群多样性和算法全局寻优能力,避免算法陷入局部最优。实验表明,算法求解所需的迭代次数少,得到的非支配解分布均匀。Map-Reduce资源调度问题的求解过程中,在收敛性和解集的多样性方面,所提算法均明显优于传统多目标粒子群算法。

To improve the computing efficiency of Map-Reduce resource scheduling, a multi-objective resource scheduling model with QoS restriction was built. The model considers the scheduling problem of both Map and Reduce phase. A chaotic multi-objective particle swarm algorithm was proposed to solve the model. The algorithm uses the information entropy theory to maintain non-dominated solution set so as to retain the diversity of solution and the uniformity of distribution. On the basis of using Sigma methods to achieve fast convergence, chaotic disturbance mechanism was intro- duced to improve the diversity of population and the ability of algorithm global optimization,which can avoid the algorithm to fall into local extremism. The experiments show that the number of iteration in the algorithm obtaining solutions is little and non-dominated solutions distribute equably. It indicates that the astringency and the diversity of solution set of this algorithm are better than the traditional multi-objective particle swarm algorithm in solving Map-Reduce resource scheduling problems.