Edge computing server placement with capacitated location allocation

• Published in: Journal of parallel and distributed computing Vol. 153; pp. 130 - 149
• Main Authors: Lähderanta, Tero, Leppänen, Teemu, Ruha, Leena, Lovén, Lauri, Harjula, Erkki, Ylianttila, Mika, Riekki, Jukka, Sillanpää, Mikko J.
• Format: Journal Article
• Language: English
• Published: Elsevier Inc 01.07.2021
• Subjects: Clustering; Facility location; k-medoid; Multi-access edge computing; Facility location; Multi-access edge computing; k-medoid; Clustering
• ISSN: 0743-7315; 1096-0848; 1096-0848
• DOI: 10.1016/j.jpdc.2021.03.007

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. •Detailed survey of existing edge server placement solutions.•Novel placement algorithm that minimizes latencies with extensive set of parameters.•Different placement scenarios, i.e. MEC and Fog, studied with QoS and QoE evaluation.•Algorithm performance evaluated with a large-scale real-world data set.•Algorithm published as an open-source software package, implemented in R.