Department of Computer Science and Engineering, R.M.K. Engineering College, Chennai, India.
School of Computing Science and Engineering, VIT University, Chennai, India.
PLoS One. 2023 Mar 13;18(3):e0282600. doi: 10.1371/journal.pone.0282600. eCollection 2023.
Cloud computing has now evolved as an unavoidable technology in the fields of finance, education, internet business, and nearly all organisations. The cloud resources are practically accessible to cloud users over the internet to accomplish the desired task of the cloud users. The effectiveness and efficacy of cloud computing services depend on the tasks that the cloud users submit and the time taken to complete the task as well. By optimising resource allocation and utilisation, task scheduling is crucial to enhancing the effectiveness and performance of a cloud system. In this context, cloud computing offers a wide range of advantages, such as cost savings, security, flexibility, mobility, quality control, disaster recovery, automatic software upgrades, and sustainability. According to a recent research survey, more and more tech-savvy companies and industry executives are recognize and utilize the advantages of the Cloud computing. Hence, as the number of users of the Cloud increases, so did the need to regulate the resource allocation as well. However, the scheduling of jobs in the cloud necessitates a smart and fast algorithm that can discover the resources that are accessible and schedule the jobs that are requested by different users. Consequently, for better resource allocation and job scheduling, a fast, efficient, tolerable job scheduling algorithm is required. Efficient Hybrid Job Scheduling Optimization (EHJSO) utilises Cuckoo Search Optimization and Grey Wolf Job Optimization (GWO). Due to some cuckoo species' obligate brood parasitism (laying eggs in other species' nests), the Cuckoo search optimization approach was developed. Grey wolf optimization (GWO) is a population-oriented AI system inspired by grey wolf social structure and hunting strategies. Make span, computation time, fitness, iteration-based performance, and success rate were utilised to compare previous studies. Experiments show that the recommended method is superior.
云计算已经成为金融、教育、互联网等各个领域不可避免的技术。云用户可以通过互联网实际访问云资源，完成云用户期望的任务。云计算服务的有效性和效率取决于云用户提交的任务以及完成任务所需的时间。通过优化资源分配和利用，任务调度对于提高云系统的效率和性能至关重要。在这种情况下，云计算提供了许多优势，例如成本节约、安全性、灵活性、移动性、质量控制、灾难恢复、自动软件升级和可持续性。根据最近的一项研究调查，越来越多的技术娴熟的公司和行业高管认识到并利用云计算的优势。因此，随着云用户数量的增加，对资源分配的监管也变得更加必要。然而，在云中调度作业需要一个智能且快速的算法，该算法可以发现可用资源，并为不同用户请求的作业进行调度。因此，为了更好地进行资源分配和作业调度，需要一种快速、高效、可容忍的作业调度算法。高效混合作业调度优化（EHJSO）利用了布谷鸟搜索优化和灰狼作业优化（GWO）。由于某些布谷鸟物种的强制性巢寄生（将卵产在其他物种的巢中），因此开发了布谷鸟搜索优化方法。灰狼优化（GWO）是一种基于群体的人工智能系统，灵感来自灰狼的社会结构和狩猎策略。本文使用了跨度、计算时间、适应性、基于迭代的性能和成功率来比较之前的研究。实验表明，所推荐的方法具有优越性。
