Edge computing is a promising computing paradigm for pushing the cloud service to the network edge. To this end, edge infrastructure providers (EIPs) need to bring computation and storage resources to the network edge and allow edge service providers (ESPs) to provision latency-critical services to users. Currently, EIPs prefer to establish a series of private edge-computing environments to serve specific requirements of users. This kind of resource provisioning mechanism severely limits the development and spread of edge computing for serving diverse user requirements. To this end, we propose an integrated resource provisioning model, named edge federation, to seamlessly realize the resource cooperation and service provisioning across standalone edge computing providers and clouds. To efficiently schedule and utilize the resources across multiple EIPs, we systematically characterize the provisioning process as a large-scale linear programming (LP) problem and transform it into an easily solved form. Accordingly, we design a dynamic algorithm to tackle the varying service demands from users. We conduct extensive experiments over the base station networks in Toronto city. Compared with the existing fixed contract model and multihoming model, edge federation can reduce the overall cost of EIPs by 23.3% to 24.5%, and 15.5% to 16.3%, respectively.

中文翻译：

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边缘联合：迈向集成服务提供模型

边缘计算是一种将云服务推向网络边缘的有前途的计算范式。为此，边缘基础设施提供商 (EIP) 需要将计算和存储资源带到网络边缘，并允许边缘服务提供商 (ESP) 为用户提供延迟关键型服务。目前，EIP 更倾向于建立一系列私有的边缘计算环境来服务用户的特定需求。这种资源提供机制严重限制了边缘计算服务于不同用户需求的发展和普及。为此，我们提出了一种名为边缘联合的集成资源供应模型，以无缝实现跨独立边缘计算提供商和云的资源协作和服务供应。为了有效地调度和利用跨多个 EIP 的资源，我们系统地将配置过程描述为大规模线性规划 (LP) 问题，并将其转换为易于解决的形式。因此，我们设计了一种动态算法来解决用户不同的服务需求。我们对多伦多市的基站网络进行了广泛的实验。与现有的固定合约模型和多宿主模型相比，边缘联合可以分别将 EIP 的整体成本降低 23.3% 至 24.5% 和 15.5% 至 16.3%。我们对多伦多市的基站网络进行了广泛的实验。与现有的固定合约模型和多宿主模型相比，边缘联合可以分别将 EIP 的整体成本降低 23.3% 至 24.5% 和 15.5% 至 16.3%。我们对多伦多市的基站网络进行了广泛的实验。与现有的固定合约模型和多宿主模型相比，边缘联合可以分别将 EIP 的整体成本降低 23.3% 至 24.5% 和 15.5% 至 16.3%。