Job scheduling based on ant colony optimization in cloud computing
Xiangqian Song, Lin Xia Gao, Jieping Wang · 2011
Effective job scheduling is critical in achieving on-demand resources allocation in dynamic cloud computing paradigm. In this paper, we proposed an Ant Colony Optimization based job scheduling algorithm, which adapts to dynamic characteristics of cloud computing and integrates specific advantages of Ant Colony Optimization in NP-hard problems. It aims to minimize job completion time based on pheromone. Experimental results obtained showed that it is a promising Ant Colony Optimization algorithm for job scheduling in cloud computing environment.