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.
翻译:边缘计算基础设施的部署需要仔细放置边缘服务器,目的是改进应用程序的延迟,减少Things系统机会性互联网上的数据传输负荷。在边缘服务器的放置方面,必须考虑边缘服务器的计算能力、现有部署预算以及硬件需求以及基本主干网络地形学。在本文中,我们彻底调查边缘服务器放置的现有文献,找出差距并提出一系列需要考虑的参数。然后,我们开发了一种新型算法,称为PACK,将服务器放置作为增强能力的地点定位问题。 PACK将服务器及其相关接入点之间的距离降到最低,同时考虑在服务器之间进行工作量平衡和共享的能力限制。此外,PACK考虑诸如优先位置和可靠性等实际问题。我们用两种不同的情况评估算法:一种是用于一般边缘计算的高能力服务器,一种是用于Fog计算机的低能力服务器。我们用在现实世界网络中收集的数据集进行评估,该数据集包括城市各接入点的密集和稀少部署。由此产生的算法和相关工具作为开放源软件公开提供。