Study On Resources Scheduling Based On ACO Algorithm And PSO Algorithm In Cloud Computing

As a new computing mode, Cloud computing, it is the development of gridcomputing, parallel computing and distributed computing, as well as the newtechnology of next generation of Internet and application. Resource Scheduling Policyin Cloud computing is an important part of cloud computing technology, it mainlyfocus on how to allocate compute nodes for the task submitted by users, how to carryon the dynamic extension of the compute nodes in the case of meeting therequirements of service quality from customers and taking the shortest execution timeto create the highest degree of load balancing, and its efficiency directly affects theperformance of the entire cloud computing environment.The ant colony algorithm and The particle swarm optimization are two swarmintelligence algorithms in the field of computational Intelligence, the former is basedon the simulation of ant colonies to collect food, and the latter simulates the processof the flock seek for food. Ant colony algorithm in the experiments of solvingtraveling salesman problem, assignment problem, scheduling, etc, turns out to bepractical. It highlights its efficiency and superiority in solving complex problems,especially in solving discrete optimization problems. Ant colony algorithm has a greatprospect of development in the future. Particle swarm optimization is a kind ofefficient parallel search algorithm, whose concept is simple and the algorithm is easyto implement. And this algorithm is good at solving continuous optimizationproblems.This paper mainly focus on the following two aspects:(1) Theories of ant colony algorithm and particle swarm algorithm will beanalyzed, and improvement of the algorithm will be made according to thedisadvantages of the two algorithms respectively. The two improved algorithms willbe combined, according to the method of foster strengths and circumvent weaknesses,and get fusion algorithm of ant colony and particle swarm optimizationalgorithm(ACO and PSO). ACO and PSO algorithm firstly form randomly severalgood solution pheromone distribution, then use ant colony algorithm based on thecumulative update pheromone to find some set of solutions, and at last get moreeffective solutions by using particle swarm algorithm to finish operation of crossover and mutation.(2) A resource scheduling strategy based on ant colony and particle swarmoptimization algorithm will be put foreword in a cloud computing environment.According to the actual situation, the ACO-PSO algorithm will be applied to a cloudcomputing platform of user tasks for resource strategy to improve the efficiency ofcloud computing resource scheduling. It is found that the cloud computing resourcescheduling strategy based on ant colony and particle swarm optimization algorithmtakes less execution time and be more effective than the algorithm based on single(ant colony algorithm or particle swarm optimization), in the same environment.