Cloud computing is a computing paradigm which meets the computational and storage demands of end users. Cloud-based data centers need to continually improve their performance due to exponential increase in service demands. Efficient task scheduling is essential part of cloud computing to achieve maximum throughput, minimum response time, reduced energy consumption and optimal utilization of resources. Bio-inspired algorithms can solve task scheduling difficulties effectively, but they need a lot of computational power and time due to high workload and complexity of the cloud environment. In this research work, Hybrid ant genetic algorithm for task scheduling is proposed. The proposed algorithm adopts features of genetic algorithm and ant colony algorithm and divides tasks and virtual machines into smaller groups. After allocation of tasks, pheromone is added to virtual machines. The proposed algorithm effectively reduces solution space by dividing tasks into groups and by detecting loaded virtual machines. Due to the minimum solution space of proposed algorithm, convergence and response time is significantly decreased. It finds a feasible scheduling solution to minimize the running time of workflows and tasks. The proposed algorithm achieved 64% decrease in execution time and 11% decrease in overall data center costs.

云计算是一种满足终端用户计算和存储需求的计算范式。由于服务需求呈指数级增长，基于云的数据中心需要不断提高其性能。高效的任务调度是云计算实现最大吞吐量、最小响应时间、降低能耗和优化资源利用的重要组成部分。仿生算法可以有效解决任务调度难题，但由于云环境的高工作量和复杂性，它们需要大量的计算能力和时间。在这项研究工作中，提出了用于任务调度的混合蚂蚁遗传算法。该算法采用遗传算法和蚁群算法的特点，将任务和虚拟机分成更小的组。任务分配后，信息素被添加到虚拟机。所提出的算法通过将任务分成组和检测加载的虚拟机来有效地减少解决方案空间。由于所提出算法的最小解空间，收敛和响应时间显着降低。它找到了一个可行的调度解决方案来最小化工作流和任务的运行时间。所提出的算法实现了 64% 的执行时间减少和 11% 的整体数据中心成本减少。