The emerging edge computing paradigm promises to provide low latency and ubiquitous computation to numerous mobile and Internet of Things (IoT) devices at the network edge. How to efficiently allocate geographically distributed heterogeneous edge resources to a variety of services is a challenging task. While this problem has been studied extensively in recent years, most of the previous work has largely ignored the preferences of the services when making edge resource allocation decisions. To this end, this paper introduces a novel bilevel optimization model, which explicitly takes the service preferences into consideration, to study the interaction between an EC platform and multiple services. The platform manages a set of edge nodes (ENs) and acts as the leader while the services are the followers. Given the service placement and resource pricing decisions of the leader, each service decides how to optimally divide its workload to different ENs. The proposed framework not only maximizes the profit of the platform but also minimizes the cost of every service. When there is a single EN, we derive a simple analytic solution for the underlying problem. For the general case with multiple ENs and multiple services, we present a Karush Kuhn Tucker based solution and a duality based solution, combining with a series of linearizations, to solve the bilevel problem. Extensive numerical results are shown to illustrate the efficacy of the proposed model.
翻译:新兴的边缘计算模式有望为网络边缘的众多移动和互联网事物(IoT)设备提供低潜值和无处不在的计算。 如何高效地将地理分布的多样化边缘资源分配给各种服务是一项具有挑战性的任务。 尽管近年来对这一问题进行了广泛研究,但大多数先前的工作在很大程度上忽视了服务在作出边际资源分配决定时的偏好。 为此,本文件引入了一个新的双级优化模式,明确考虑到服务偏好,以研究欧盟委员会平台和多种服务之间的相互作用。平台管理一套边端节点,并充当领导者,而服务追随者则是服务追随者。鉴于领导人的服务安排和资源定价决定,每个服务部门都决定如何将其工作量与不同的EN部门进行最佳分工。拟议框架不仅最大限度地增加平台的利润,而且将每项服务的成本降到最低。如果有一个单一的EN,我们就会为根本问题找到一个简单的解析解决方案。 对于有多个ENs和多个服务的一般案例,我们提出了一个基于Karush Kuhnuck的模型解决方案, 以及一个基于双向级解决方案的双向级解决方案。