Abstract: A task scheduling strategy based on the dynamically adaptive ant colony algorithm was proposed for the first time to solve the drawbacks like slow convergence and easily falling into local optimal that have long existed in the ant colony algorithm. Chaos disruption was introduced when selecting the resource node, the pheromone evaporation factors were adjusted adaptively based on nodes pheromone and the pheromone were updated dynamically according to the solutions performance. When the number of tasks was greater than 150, compared with the dynamically adaptive ant colony algorithm and ant colony algorithm, time efficiency could be maximally improved up to 319% and resource load was 0.51.The simulation results prove that the proposed algorithm is suitable for improving convergence rate and the global searching ability.

Key words: cloud computing, ant colony algorithm, dynamic adaption, task scheduling, chaos disruption

摘要： 针对蚁群算法求解云计算任务调度问题存在收敛速度慢和容易陷入局部最优解的缺陷，提出一种动态自适应蚁群算法的云计算任务调度策略。算法在选择资源节点中引入混沌扰乱，依据节点信息素浓度自适应调整信息素挥发因子，由解的优劣性动态更新信息素。当任务数量超过150时，动态自适应蚁群算法与蚁群算法结果相比较，时间效率最大提高319%，资源负载率为0.51。仿真结果表明，所提算法提高了解的收敛速度和全局搜索能力。

关键词: 云计算, 蚁群算法, 动态自适应, 任务调度, 混沌扰乱