Edge computing, as an extension of cloud computing, distributes computing and storage resources from centralized cloud to distributed edge servers, to power a variety of applications demanding low latency, e.g., IoT services, virtual reality, real-time navigation, etc. From an app vendor's perspective, app data needs to be transferred from the cloud to specific edge servers in an area to serve the app users in the area. However, according to the pay-as-you-go business model, distributing a large amount of data from the cloud to edge servers can be expensive. The optimal data distribution strategy must minimize the cost incurred, which includes two major components, the cost of data transmission between the cloud to edge servers and the cost of data transmission between edge servers. In the meantime, the delay constraint must be fulfilled - the data distribution must not take too long. In this article, we make the first attempt to formulate this Edge Data Distribution (EDD) problem as a constrained optimization problem from the app vendor's perspective and prove its $\mathcal {NP}$NP-hardness. We propose an optimal approach named EDD-IP to solve this problem exactly with the Integer Programming technique. Then, we propose an $O(k)$O(k)-approximation algorithm named EDD-A for finding approximate solutions to large-scale EDD problems efficiently. EDD-IP and EDD-A are evaluated on a real-world dataset and the results demonstrate that they significantly outperform three representative approaches.

中文翻译：

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边缘计算中经济高效的应用数据分发

边缘计算作为云计算的延伸，将计算和存储资源从集中式云分配到分布式边缘服务器，为物联网服务、虚拟现实、实时导航等各种要求低延迟的应用提供动力。从应用供应商的角度来看，应用数据需要从云端传输到区域内特定的边缘服务器，为区域内的应用用户提供服务。然而，根据现收现付的商业模式，将大量数据从云端分发到边缘服务器可能会很昂贵。最优的数据分发策略必须最小化所产生的成本，它包括两个主要组成部分，云到边缘服务器之间的数据传输成本和边缘服务器之间的数据传输成本。同时，必须满足延迟约束 - 数据分发不得花费太长时间。在本文中，我们首次尝试从应用程序供应商的角度将此边缘数据分布 (EDD) 问题表述为约束优化问题，并证明其$\mathcal {NP}$NP-硬度。我们提出了一种名为 EDD-IP 的最佳方法，以使用整数规划技术准确地解决这个问题。然后，我们提出一个$O(k)$哦(克)- 名为 EDD-A 的近似算法，用于有效地找到大规模 EDD 问题的近似解。EDD-IP 和 EDD-A 在真实世界的数据集上进行评估，结果表明它们明显优于三种代表性方法。