云资源中多目标集成蚁群优化调度算法

针对云计算环境的复杂性和云资源的不确定性，提出多目标集成蚁群优化调度算法。采用熵度量云资源的不确定性，进行信息素全局更新，以提高算法收敛速度；将Min-min算法得出的任务预期最小完成时间作为启发信息，以实现最小调度时间；在信息素局部更新时加入负载系数，根据当前负载情况调节信息素，满足负载均衡需求，同时在更新时考虑信息素扩散因素，不仅计算当前节点还考虑周遭节点信息素情况，可增强蚂蚁间协作，提高最优解的性能。改进后算法比原始蚁群算法降低了算法复杂度，提高了最优解精度。云仿真系统实验测试表明改进算法在调度时间、负载均衡等方面表现均优于其他算法。

Multi-objective integrated ant colony optimization scheduling algorithm based on cloud resource

With regard to the complexity of cloud computing and uncertainty of cloud resources,an integrated multi-objective ant colony optimization scheduling algorithm was proposed.Using entropy to measure the uncertainty of cloud resources,and updating the global pheromone,the algorithm’s convergence rate could be improved.In order to achieve the minimum activation time,the expected minimum completion time calculated by Min-min algorithm was used as heuristic information.When the local pheromone was updated partly,the load factor was added according to the current load regulation of pheromone,to achieve the load balance.Collaboration between ants could be enhanced and the performance of the optimal solution would be improved,considering the pheromone diffusion factors,that is to consider not only the current node pheromone but also neighbor node pheromone.The advanced ant colony algorithm reduced the complexity of the algorithm better than the original algorithm,and improved the optimal solution accuracy.The experiments of the cloud simulation system prove that the performance of time scheduling and load balancing of the proposed algorithm is better than other algorithms.