Research on Cloud Task Scheduling Based on Load Balancing Ant Colony Optimization

Abstract

For the problem of resource load unevenness of task scheduling in cloud computing, this paper proposes Load Balancing Ant Colony Optimization (LBACO) based on load conditions of virtual machines. At the same time, the pheromone initialization, heuristic function, state transition probability, and pheromone updating method in the ant colony algorithm(ACO) are improved. The improved scheduling strategy was experimented on the CloudSim platform. The experimental results show that compared with ACO and RR algorithms, the LBACO algorithm achieves better performance in terms of makespan of task sets and the system load balance.