基于改进克隆选择算法的云计算任务调度算法

TASK SCHEDULING ALGORITHM IN CLOUD COMPUTING BASED ON IMPROVED CLONAL SELECTION ALGORITHM

在云计算中,系统要面对庞大的用户群,处理大量任务以及数据。如何对云环境中的大量任务进行高效的调度、满足用户需求成为了云计算中所要解决的重要问题。针对云计算的并行编程模型,借鉴生物免疫系统的克隆选择机制,利用生物工程中基因重组技术,提出一种基于基因重组的克隆选择算法,将此算法应用到云环境的任务调度问题中,可以确定最佳的任务调度方案。通过仿真实验将此算法与传统克隆选择算法进行比较,结果证明此算法的收敛速度与收敛精度均优于传统克隆选择算法,并且通过此算法可以确定较优的任务调度策略,是一种云计算环境中有效的任务调度算法。

In cloud computing,the system has to face huge numbers of users and processes massive tasks and data.How to efficiently schedule enormous tasks in cloud environment and to meet users needs become the important issues to be resolved in cloud computing.In light of parallel programming model of cloud computing,we bring up a kind of gene recombination-based clonal selection algorithm by learning from biological immune system the clonal selection mechanism and making use of gene recombination technology of biological engineering,and apply this algorithm to task scheduling problem in cloud environment,thus the best task scheduling scheme can be determined.This improved clonal selection algorithm is compared with traditional one through simulation experiment.The experimental result proves that the improved algorithm outperforms the traditional one in both convergence speed and accuracy.Furthermore,through this algorithm it is able to determine an optimal task scheduling strategy,it is an effective task scheduling algorithm in cloud computing.