The deployment of edge computing infrastructure requires a careful placement of the edge servers, with an aim to improve application latencies and reduce data transfer load in opportunistic Internet of Things systems. In the edge server placement, it is important to consider computing capacity, available deployment budget, and hardware requirements for the edge servers and the underlying backbone network topology. In this paper, we thoroughly survey the existing literature in edge server placement, identify gaps and present an extensive set of parameters to be considered. We then develop a novel algorithm, called PACK, for server placement as a capacitated location–allocation problem. PACK minimizes the distances between servers and their associated access points, while taking into account capacity constraints for load balancing and enabling workload sharing between servers. Moreover, PACK considers practical issues such as prioritized locations and reliability. We evaluate the algorithm in two distinct scenarios: one with high capacity servers for edge computing in general, and one with low capacity servers for Fog computing. Evaluations are performed with a data set collected in a real-world network, consisting of both dense and sparse deployments of access points across a city area. The resulting algorithm and related tools are publicly available as open source software.

边缘计算基础架构的部署需要仔细放置边缘服务器，以提高应用程序延迟并减少机会性物联网系统中的数据传输负载。在边缘服务器的放置中，重要的是要考虑计算能力，可用部署预算以及边缘服务器和基础骨干网络拓扑的硬件要求。在本文中，我们彻底调查了边缘服务器放置方面的现有文献，确定了差距，并提出了要考虑的广泛参数集。然后，我们针对服务器放置开发了一种称为PACK的新颖算法，将其作为一个受限的位置分配问题。PACK可以最大程度地减少服务器及其关联的访问点之间的距离，同时考虑容量限制以实现负载平衡并实现服务器之间的工作负载共享。此外，PACK考虑了诸如优先位置和可靠性之类的实际问题。我们在两种不同的情况下评估该算法：一种情况是通常使用高容量服务器进行边缘计算，另一种情况是使用低容量服务器进行雾计算。评估是使用现实网络中收集的数据集执行的，该数据集包括整个城市区域中接入点的密集部署和稀疏部署。生成的算法和相关工具可作为开源软件公开获得。一台用于雾计算的低容量服务器。评估是使用现实网络中收集的数据集执行的，该数据集包括整个城市区域中接入点的密集部署和稀疏部署。生成的算法和相关工具可作为开源软件公开获得。一台用于雾计算的低容量服务器。评估是使用现实网络中收集的数据集执行的，该数据集包括整个城市区域中接入点的密集部署和稀疏部署。生成的算法和相关工具可作为开源软件公开获得。