A Multi-objective Optimization Algorithm for Cloud Computing Task Scheduling Based on Improved Ant Colony Algorithm

An improved ant colony task scheduling algorithm (TCL-ACO) based on improved ant colony algorithm is presented, which combines the shortest completion time of task, cost and load balance.Firstly, the constraint function of task completion time and costs and the load balance function are defined according to the characteristics of task scheduling under cloud computing. At the same time, we improved the initial pheromone, heuristic function and pheromone update method of ant colony algorithm.Then, using the improved ant colony algorithm to solve the objective constraint function, the global optimal solution is obtained.Finally, the simulations are carried out under the cloudsim and compared with the Min-Min algorithm and the ACO algorithm. The experimental results show that the algorithm is superior to the two algorithms in terms of cost, task execution time and load balance.