University of Oulu

Tero Lähderanta, Teemu Leppänen, Leena Ruha, Lauri Lovén, Erkki Harjula, Mika Ylianttila, Jukka Riekki, Mikko J. Sillanpää, Edge computing server placement with capacitated location allocation, Journal of Parallel and Distributed Computing, Volume 153, 2021, Pages 130-149, ISSN 0743-7315, https://doi.org/10.1016/j.jpdc.2021.03.007

Edge computing server placement with capacitated location allocation

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Author: Lähderanta, Tero1; Leppänen, Teemu2; Ruha, Leena1,3;
Organizations: 1Research Unit of Mathematical Sciences, University of Oulu, Finland
2Center for Ubiquitous Computing, University of Oulu, Finland
3Natural Resources Institute Finland, Oulu, Finland
4Centre for Wireless Communications, University of Oulu, Finland
Format: article
Version: published version
Access: open
Online Access: PDF Full Text (PDF, 3.8 MB)
Persistent link: http://urn.fi/urn:nbn:fi-fe2021062239366
Language: English
Published: Elsevier, 2021
Publish Date: 2021-06-22
Description:

Abstract

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.

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Series: Journal of parallel and distributed computing
ISSN: 0743-7315
ISSN-E: 1096-0848
ISSN-L: 0743-7315
Volume: 153
Pages: 130 - 149
DOI: 10.1016/j.jpdc.2021.03.007
OADOI: https://oadoi.org/10.1016/j.jpdc.2021.03.007
Type of Publication: A1 Journal article – refereed
Field of Science: 113 Computer and information sciences
213 Electronic, automation and communications engineering, electronics
Subjects:
Funding: This research is supported by Academy of Finland 6Genesis Flagship (grant 318927), the Infotech Oulu research institute, Finland, the Future Makers program of the Jane and Aatos Erkko Foundation, Finland and the Technology Industries of Finland Centennial Foundation, by Academy of Finland Profi 5 funding for mathematics and AI: data insight for high-dimensional dynamics (grant 326291), and the personal grant for Lauri Lovén on Edge-native AI research by the Tauno Tönning foundation, Finland.
Academy of Finland Grant Number: 318927
326291
Detailed Information: 318927 (Academy of Finland Funding decision)
326291 (Academy of Finland Funding decision)
Copyright information: © 2021 The Author(s). Published by Elsevier Inc. This is an open access article under the CC BY license (http://creativecommons.org/licenses/by/4.0/).
  https://creativecommons.org/licenses/by/4.0/