Data-Driven Capacity Planning for Vehicular Fog Computing

Mao, Wencan, Akgul, Ozgur Umut, Mehrabidavoodabadi, Abbas, Cho, Byungjin, Xiao, Yu and Yla-Jaaski, Antti (2022) Data-Driven Capacity Planning for Vehicular Fog Computing. IEEE Internet of Things Journal. pp. 1-17. ISSN 2372-2541 (In Press)

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Official URL: https://doi.org/10.1109/JIOT.2022.3143872

Abstract

The strict latency constraints of emerging vehicular applications make it unfeasible to forward sensing data from vehicles to the cloud for processing. To shorten network latency, vehicular fog computing (VFC) moves computation to the edge of the Internet, with the extension to support the mobility of distributed computing entities (a.k.a fog nodes). In other words, VFC proposes to complement stationary fog nodes co-located with cellular base stations with mobile ones carried by moving vehicles (e.g., buses). Previous works on VFC mainly focus on optimizing the assignments of computing tasks among available fog nodes. However, capacity planning, which decides where and how much computing resources to deploy, remains an open and challenging issue. The complexity of this problem results from the spatio-temporal dynamics of vehicular traffic, varying computing resource demand generated by vehicular applications, and the mobility of fog nodes. To solve the above challenges, we propose a data-driven capacity planning framework that optimizes the deployment of stationary and mobile fog nodes to minimize the installation and operational costs under the quality-of-service constraints, taking into account the spatio-temporal variation in both demand and supply. Using real-world traffic data and application profiles, we analyze the cost efficiency potential of VFC in the long term. We also evaluate the impacts of traffic patterns on the capacity plans and the potential cost savings. We find that high traffic density and significant hourly variation would lead to dense deployment of mobile fog nodes and create more savings in operational costs in the long term.

Item Type: Article
Additional Information: Funding information: This project has received funding from the European Union’s Horizon 2020 research and innovation programme under grant agreement No. 825496 and No. 815191, and Academy of Finland under grant number 317432 and 318937.
Uncontrolled Keywords: Capacity planning, vehicular fog computing (VFC), spatio-temporal analysis, application profiling, integer linear programming (ILP), techno-economic analysis
Subjects: G400 Computer Science
G500 Information Systems
Department: Faculties > Engineering and Environment > Computer and Information Sciences
Depositing User: John Coen
Date Deposited: 09 Feb 2022 11:16
Last Modified: 10 Feb 2022 10:06
URI: http://nrl.northumbria.ac.uk/id/eprint/48416

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