Research On Hybrid Ant Colony Algorithm For Task Scheduling In Cloud Environment

With the development of Internet technology,the scale of cloud users is constantly increasing.A large number of tasks are concentrated in the cloud,which leads to a long waiting time for tasks and a decline in the performance of cloud computing system.The main reason for this phenomenon is the unreasonable task scheduling,so it is of great significance to study the task scheduling algorithm.Ant Colony Optimization(ACO)algorithm has a good performance in finding the optimal path,and is often used to solve the task scheduling problem in cloud environment.However,it is easy to fall into local extreme value and slow convergence speed in the process of solving.In view of the above problems,this paper(1)improves the heuristic function and pheromone concentration of traditional ant colony algorithm to solve the problem that the algorithm is easy to fall into local extremum.Firstly,a load balancing factor is introduced into the heuristic function to adjust the assignment probability of resources with strong computing power.Secondly,pheromone correction coefficient is introduced to update the accumulation of pheromone concentration in resources in time,and the positive feedback mechanism of ant colony was better realized.(2)Design a hybrid ant colony algorithm(QACO)to solve the problem of slow convergence in the initial stage of the algorithm.Firstly,the task priority was set.Secondly,the initial pheromone of ant colony is improved by introducing q-learning algorithm to enhance the guiding effect of pheromone concentration on ant resource selection.Finally,the improved ant colony algorithm is combined to realize the task scheduling in cloud environment.By using cloud simulation platform CloudSim to simulate workflow task scheduling,the QACO algorithm,ACO algorithm,resource state-based adaptive ant colony optimization(MACO)algorithm and the classical bionic algorithm particle swarm optimization(PSO)algorithm are compared.Experimental results show that the QACO algorithm improves the execution time of Montage tasks by 14.98%,4.90%and 17.26%compared with ACO algorithm,MACO algorithm and PSO algorithm,respectively.In terms of resource utilization,QACO algorithm has the best resource utilization.Compared with ACO algorithm,MACO algorithm and PSO algorithm,the accuracy of QACO algorithm to solve the objective function value is improved by 24.36%,12.48%and 18.68%,respectively,and the convergence speed of the algorithm is also significantly increased.In order to verify the effectiveness of QACO algorithm,the scheduling results of workflow tasks LIGO and CyberShake are also better than the other three algorithms.