Cloud computing has now become the most effective platform for providing elastic and on-demand provisioning of high performance heterogeneous and homogeneous computing services basis on pay-per-use in the field high performance computing world. Task scheduling in cloud computing devotes researchers' attention to provide the optimal solution to this NP-Complete problem. An optimized task scheduling algorithm optimizes the cloud system's performance and generates the maximum profit for the cloud service provider. To overcome this issue in cloud computing, Authors developed a hybrid multi-faceted task scheduling algorithm in this research work. The proposed algorithm exploited the features of standard particle swarm optimization (PSO) and Ant Colony Optimization (ACO) technique. The PSO technique provides the best global optimal solution, whereas ACO offers the best local solution. To validate the results of the developed algorithm, performed a comparison of the makespan, cost, and resource utilization rate parameters against the well-known exiting four algorithms for the computer-generated tasks set in the cloud environment through a simulation experiment. The comparison results showed that the proposed algorithm reduces the makespan time and computation cost as well as increases resource utilization rate.

云计算现在已成为最有效的平台，用于在现场高性能计算世界中按使用付费按需提供弹性和按需配置高性能异构和同类计算服务。云计算中的任务调度致力于研究人员的注意力，以为该NP-Complete问题提供最佳解决方案。优化的任务调度算法可优化云系统的性能，并为云服务提供商带来最大的利润。为了克服云计算中的这个问题，作者在这项研究工作中开发了一种混合的多面任务调度算法。该算法利用了标准粒子群算法（PSO）和蚁群算法（ACO）的特点。PSO技术提供了最佳的全局最优解决方案，而ACO提供了最佳的本地解决方案。为了验证所开发算法的结果，通过模拟实验，将构建时间，成本和资源利用率参数与云计算环境中计算机生成的任务集的已知的四种现有算法进行了比较。比较结果表明，该算法减少了建立时间和计算成本，提高了资源利用率。