The rapid and widespread adoption of internet of things-related services advances the development of the cloud-edge framework, including multiple cloud datacenters (CDCs) and edge micro-datacenters (EDCs). This paper aims to apply analytical modeling techniques to assess the effectiveness of cloud-edge computing resource allocation policies from the perspective of improving the performance of cloud-edge service. We focus on two types of physical device (PD)-allocation policies that define how to select a PD from a CDC/EDC for service provision. The first is randomly selecting a PD, denoted as RandAvail. The other is denoted as SEQ, in which an available idle PD is selected to serve client requests only after the waiting queues of all busy PDs are full. We first present the models in the case of an On–Off request arrival process and verify the approximate accuracy of the proposed models through simulations. Then, we apply analytical models for comparing RandAvail and SEQ policies, in terms of request rejection probability and mean response time, under various system parameter settings.

中文翻译：

---

基于模型的云边缘计算资源分配策略比较

物联网相关服务的迅速广泛采用推动了云边缘框架的发展，其中包括多个云数据中心（CDC）和边缘微数据中心（EDC）。本文旨在从提高云边缘服务性能的角度出发，应用分析建模技术来评估云边缘计算资源分配策略的有效性。我们关注两种类型的物理设备（PD）分配策略，这些策略定义了如何从CDC / EDC中选择PD来提供服务。首先是随机选择一个PD，表示为RandAvail。另一个表示为SEQ，其中仅在所有繁忙PD的等待队列已满后，才选择可用的空闲PD来满足客户端请求。我们首先在开-关请求到达过程中介绍模型，并通过仿真验证所提出模型的大致准确性。然后，我们在各种系统参数设置下，应用分析模型比较RandAvail和SEQ策略的请求拒绝概率和平均响应时间。