Cloud computing has the advantage of providing flexibility, high-performance, pay-as-you-use, and on-demand service. One of the important research issues in cloud computing is task scheduling. The purpose of scheduling is to assign tasks to available resources while providing optimization on some objectives. Tasks have diversified characteristics, and resources are heterogeneous. These properties make task scheduling an NP-complete problem. In this study, metaheuristic and hybrid metaheuristic algorithms are developed for task scheduling problems in cloud computing environments. We have developed genetic algorithm (GA), differential evolution (DE), and simulated annealing (SA) based metaheuristic algorithms, which are also combined with a greedy approach (GR). In addition to this, we have developed hybrid metaheuristics algorithms, called DE-SA and GA-SA, which are also combined with a greedy approach. The proposed approaches are evaluated in terms of completion time and load balancing of virtual machines. In terms of average completion time, as the number of tasks increases, it has been observed that the DESA algorithm outperforms the solely used DE and SA algorithms. In addition, experiments show that hybrid algorithms improve both the average completion time and the average standard deviation of virtual machine loads for some task groups.

中文翻译：

---

云计算环境的元启发式任务调度算法

云计算具有提供灵活性、高性能、按需付费和按需服务的优势。云计算的重要研究问题之一是任务调度。调度的目的是将任务分配给可用资源，同时对某些目标进行优化。任务具有多样化的特点，资源是异构的。这些属性使任务调度成为一个 NP 完全问题。在这项研究中，针对云计算环境中的任务调度问题开发了元启发式和混合元启发式算法。我们开发了基于遗传算法 (GA)、差分进化 (DE) 和模拟退火 (SA) 的元启发式算法，这些算法还与贪心方法 (GR) 相结合。除此之外，我们还开发了混合元启发式算法，称为 DE-SA 和 GA-SA，它们也与贪心方法相结合。所提出的方法在完成时间和虚拟机的负载平衡方面进行了评估。在平均完成时间方面，随着任务数量的增加，已经观察到 DESA 算法优于单独使用的 DE 和 SA 算法。此外，实验表明，混合算法提高了某些任务组的平均完成时间和虚拟机负载的平均标准偏差。