In recent years, the fast development of mobile communications and cloud systems has substantially promoted edge computing. By pushing server resources to the edge, mobile service providers can deliver their content and services with enhanced performance, and mobile-network carriers can alleviate congestion in the core networks. Although edge computing has been attracting much interest, most current research is application-specific, and analysis is lacking from a business perspective of edge cloud providers (ECPs) that provide general-purpose edge cloud services to mobile service providers and users. In this article, we present a vision of general-purpose edge computing realized by multiple interconnected edge clouds, analyzing the business model from the viewpoint of ECPs and identifying the main issues to address to maximize benefits for ECPs. Specifically, we formalize the long-term revenue of ECPs as a function of server-resource allocation and public data-placement decisions subject to the amount of physical resources and inter-cloud data-transportation cost constraints. To optimize the long-term objective, we propose an online framework that integrates the drift-plus-penalty and primal-dual methods. With theoretical analysis and simulations, we show that the proposed method approximates the optimal solution in a challenging environment without having future knowledge of the system.

中文翻译：

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通用边缘计算的在线编排机制


近年来，移动通信和云系统的快速发展极大地促进了边缘计算。通过将服务器资源推向边缘，移动服务提供商可以提供性能增强的内容和服务，移动网络运营商可以缓解核心网络的拥塞。尽管边缘计算引起了广泛关注，但目前大多数研究都是针对特定应用的，缺乏从向移动服务提供商和用户提供通用边缘云服务的边缘云提供商（ECP）的业务角度进行分析。在本文中，我们提出了通过多个互连边缘云实现的通用边缘计算的愿景，从 ECP 的角度分析了业务模型，并确定了需要解决的主要问题，以最大限度地提高 ECP 的利益。具体来说，我们将 ECP 的长期收入形式化为服务器资源分配和公共数据放置决策的函数，并受到物理资源量和云间数据传输成本限制的影响。为了优化长期目标，我们提出了一个集成了漂移加惩罚和原始对偶方法的在线框架。通过理论分析和模拟，我们表明所提出的方法在具有挑战性的环境中近似最优解决方案，而无需了解系统的未来知识。