In the context of cloud computing, one problem that is frequently encountered is task scheduling. This problem has two primary implications, which are the planning of tasks on virtual machines and the attenuation of performance. In order to address the problem of task scheduling in cloud computing, requisite nontraditional optimization attitudes to attain the optima of the problem, the present paper puts forth a hybrid multiple-objective approach called hybrid grey wolf and whale optimization (HGWWO) algorithms, that integrates two algorithms, namely, the grey wolf optimizer (GWO) and the whale optimization algorithm (WOA), with the purpose of conjoining the advantages of each algorithm for minimizing costs, energy consumption, and total execution time needed for task implementation, beside that improving the use of resources. Assessment of the aims of the proposed approach is carried out with the help of the tool known as CloudSim. As pointed out by the results of the experimental work undertaken, the proposed approach has the capability of performing at a superior level by comparison to the original algorithms GWO and WOA on their own with regard to costs, energy consumption, makespan, use of resources, and degree of imbalance.

中文翻译：

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一种基于灰狼和鲸鱼优化算法的混合方法解决云任务调度问题

在云计算的背景下，经常遇到的一个问题就是任务调度。这个问题有两个主要含义，即虚拟机上的任务规划和性能衰减。为了解决云计算中的任务调度问题，需要非传统的优化态度才能达到问题的最优，本文提出了一种混合多目标方法，称为混合灰狼和鲸鱼优化（HGWWO）算法，它集成了两种算法，即灰狼优化器（GWO）和鲸鱼优化算法（WOA），目的是结合每种算法的优点，最大限度地降低任务执行所需的成本、能耗和总执行时间，同时提高资源的使用。建议方法的目标评估是在称为 CloudSim 的工具的帮助下进行的。正如所进行的实验工作的结果所指出的那样，与原始算法 GWO 和 WOA 相比，所提出的方法在成本、能耗、完工时间、资源使用、和不平衡的程度。