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A Genetic-Ant-Colony Hybrid Algorithm for Task Scheduling in Cloud System

机译:云系统任务调度的遗传-蚁群混合算法

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As the task load of cloud system grows bigger, it becomes very important to design an efficiency task scheduling algorithm. This paper proposes a task scheduling algorithm based on genetic algorithm and ant colony optimization algorithm. The hybrid task scheduling algorithm can help the cloud system to complete users' tasks faster. Simulation experiment results in CloudSim show that, comparing with genetic algorithm and ant colony optimization algorithm alone, the hybrid algorithm has better performance in the aspects of load balancing and optimal time span.
机译:随着云系统任务负载的增大,设计高效的任务调度算法变得非常重要。提出了一种基于遗传算法和蚁群优化算法的任务调度算法。混合任务调度算法可以帮助云系统更快地完成用户的任务。 CloudSim中的仿真实验结果表明,与单独的遗传算法和蚁群优化算法相比,混合算法在负载均衡和最佳时间跨度方面具有更好的性能。

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