在云计算环境中用户数量众多，用户提交的任务总量非常庞大，如何调度这些海量任务使其高效合理地完成成为云计算研究的关键。针对云计算环境的特点，对粒子群和蚁群算法进行改进，提出一种融合二者的任务调度算法。该算法采用粒子群算法进行前期迭代，迭代完成后选取一定数量的优良粒子生成蚁群算法的初始信息素，蚁群算法利用已生成的初始信息素进行后期迭代，并求得最终的任务调度结果。仿真结果表明，该算法优于粒子群算法和蚁群算法，任务的总完成时间明显减少，是一种高效的调度算法。%In cloud computing environment,there are a large number of users and tasks to be submitted by users. In order to make these tasks to be completed efficiently,how to schedule the tasks becomes the key of cloud computing. According to characteristics of cloud computing environment,improving Particle Swarm Optimization ( PSO) and Ant Colony Optimization ( ACO) ,a task scheduling algo-rithm combining PSO with ACO. It uses PSO to carry out the previous iteration,and selects a certain number of fine particles to generate the initial pheromone of ACO which carries out the post iteration by it,and then the final task scheduling result is obtained. The simulation shows that the algorithm is better than PSO and ACO,and decreases the total task completion time. It is an effective task scheduling algo-rithm.
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