资源调度与分配算法的优化

S. Rahul, Vinay Bhardwaj
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摘要

物联网(IoT)的迅速发展,无处不在的联网计算机设备将渗透到商业和私人领域。设备通过互联网访问,资源保存在云中。由于物联网设备功率有限,它们不能直接连接到互联网或云。雾计算用于将物联网设备连接到互联网或云。雾计算是一种云计算扩展,其中在边缘设备或相邻的本地化数据中心或云上执行处理。影响雾计算性能的最关键因素是资源管理。由于资源的限制,资源规划是物联网云雾系统的主要关注点。许多研究都引入了优化技术,如max min, FCFS, Round robin, min min, GA。因此,物联网雾云系统节省了时间、金钱和能源。资源调度的目标是从物理上可用的各种匹配资源中选择最优资源。在调度之后,执行资源分配,目标是将选定的资源分配给作业。在云环境中,资源分配是指将可用的虚拟机实例分配给工作负载的过程。本文从平均等待时间、平均响应时间和完工时间等参数出发,对适合隔离的Min-Min、Round - robin、FCFS、SJF进行了讨论和分析。最后,我们提出了一种混合算法,并将推荐算法与现有算法的有效性进行了比较
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Optimization of Resource Scheduling and Allocation Algorithms
The expeditious growth of the Internet of Things (IoT), ubiquitous, networked computer gadgets will pervade commercial and private areas. Devices are accessed through the Internet, and resources are kept in the cloud. Because IoT devices are power-constrained, they cannot connect to the internet or the cloud directly. Fog Computing is used to link IoT devices to the Internet or the Cloud. Fog computing is a cloud computing extension in which processing is performed on edge devices or adjacent localized data centers or cloudlets. The most crucial factors influencing fog computing performance is resource management. Due to resource constraints, resource planning is a major concern in the IoT-Cloud-Fog system. Numerous studies have introduced optimization techniques like max min , FCFS, Round robin, min min, GA. As a result of this, the IoT-Fog-Cloud System saves time, money, and energy. The goal of resource scheduling is to choose the optimal resource from a variety of matching resources which are physically available. Following scheduling, resource allocation is carried out, with the goal of allocating the chosen resource to the job. In a Cloud context, resource allocation refers to the process of assigning available virtual machine instances to workloads. This paper discusses and analyzes Min-Min, Round robin, FCFS, SJF suitable for secluding considering the following parameters average waiting time , average response time and makespan. Finally, we propose a hybrid algorithm and compare the recommended algorithm with the existing algorithm in terms of effectiveness
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