For many years, job scheduling in cloud computing has been researched to improve and optimize the environment. Although many researchers have worked on the issue of job scheduling, however, a comprehensive approach still misses out on various fronts like consideration of multi objective functions, handling the problem of local minima, and best resource utilization. An attempt has been made in the paper to present a reliable and comprehensive scheduling approach based on the meta-heuristic for the cloud computing environment. The proposed algorithm imitates the behavior of Rock Hyrax optimization for scheduling the jobs in a dynamic and heterogeneous cloud environment by considering the quality of service parameters like makespan time and energy consumption of data centers. The result establishes the claim that the proposal presented in this paper can schedule jobs in a dynamic environment on the virtual machine by keeping energy consumption low. The proposal is implemented through an experimental setup in the CloudSim environment and considered for variable jobs. The proposed algorithm for scheduling in the cloud environment is evaluated both qualitatively and quantitatively by considering both jobs and virtual machines statically and dynamically. The proposed algorithm is also compared with the prevalent approaches proposed in the past and shows better results. Our results indicate that the proposed meta-heuristic algorithm based on Rock Hyrax has lowered the makespan time by 5–15% and reduces energy consumption by 4–12%.

多年来，人们一直在研究云计算中的作业调度以改善和优化环境。尽管许多研究人员已经研究了工作调度问题，但是，综合方法仍然在各个方面都被忽略了，例如考虑多目标函数，处理局部极小值问题和最佳资源利用。本文已尝试提出一种基于元启发式的云计算环境可靠而全面的调度方法。该算法通过考虑服务质量参数（如建立时间和数据中心的能耗）来模仿Rock Hyrax优化的行为，以在动态异构云环境中调度作业。结果证明，本文提出的建议可以通过降低能耗来在虚拟机上的动态环境中调度作业。该提案是通过CloudSim环境中的实验性设置实施的，并考虑了可变作业。通过静态和动态地同时考虑作业和虚拟机，定性和定量地评估了所提出的云环境中调度算法。将该算法与过去提出的流行方法进行了比较，并显示了更好的结果。我们的结果表明，基于Rock Hyrax提出的元启发式算法已将建立时间缩短了5–15％，并将能耗降低了4–12％。