Capacity Planning for Vehicular Fog Computing

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Journal Title
Journal ISSN
Volume Title
School of Electrical Engineering | Doctoral thesis (article-based) | Defence date: 2023-11-03
Date
2023
Major/Subject
Mcode
Degree programme
Language
en
Pages
68 + app. 70
Series
Aalto University publication series DOCTORAL THESES, 172/2023
Abstract
The strict latency constraints of emerging vehicular applications make it unfeasible to forward sensing data from vehicles to the cloud for processing. Fog computing shortens the network latency by moving computation close to the location where the data is generated. Vehicular fog computing (VFC) proposes to complement stationary fog nodes co-located with cellular base stations (i.e., CFNs) with mobile ones carried by vehicles (i.e., VFNs) in a cost-efficient way. Previous works on VFC have mainly focused 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, the uncertainty in the computational demand, and the trade-off between the quality of service (QoS) and cost expenditure. This dissertation focuses on capacity planning for VFC. The objective of capacity planning is to maximize the techno-economic performance of VFC in terms of profit and QoS. To address the spatial-temporal dynamics of vehicular traffic, this dissertation presents a capacity planning solution for VFC that jointly decide the location and number of CFNs together with the route and schedule of VFNs carried by buses. Such a long-term planning solution is supposed to be updated seasonally according to the traffic pattern and bus timetables. To address the uncertainty in the computational resource demand, this dissertation presents two capacity planning solutions for VFC that dynamically schedule the routes of VFNs carried by taxis in an on-demand manner. Such a short-term planning solution is supposed to be updated within minutes or even seconds. To evaluate the techno-economic performance of our capacity planning solutions, an open-source simulator was developed that takes real-world data as inputs and simulates the VFC scenarios in urban environments. The results of this dissertation can contribute to the development of edge and fog computing, the Internet of Vehicles (IoV), and intelligent transportation systems (ITS).
Description
Supervising professor
Yu, Xiao, Prof., Aalto University, Department of Communications and Networking, Finland; Ylä-Jääski, Antti, Prof., Aalto University, Department of Computer Science, Finland
Thesis advisor
Akgül, Özgür Umut, Nokia Corporation, Finland
Keywords
computing, fog computing, capacity
Other note
Parts
  • [Publication 1]: Wencan Mao, Ozgur Umut Akgul, Abbas Mehrabi, Byungjin Cho, Yu Xiao, and Antti Ylä-Jääski. Data-Driven Capacity Planning for Vehicular Fog Computing. Accepted for publication in IEEE Internet of Things Journal, Volume: 9, Issue: 15, Pages: 13179 - 13194, August 2022.
    Full text in Acris/Aaltodoc: http://urn.fi/URN:NBN:fi:aalto-202202021664
    DOI: 10.1109/JIOT.2022.3143872 View at publisher
  • [Publication 2]: Ozgur Umut Akgul, Wencan Mao, Byungjin Cho, and Yu Xiao. VFogSim: A Data-driven Platform for Simulating Vehicular Fog Computing Environment. Accepted for publication in IEEE Systems Journal, Volume: 17, Issue: 3, Pages: 5002 - 5013, September 2023. August 2022.
    Full text in Acris/Aaltodoc: http://urn.fi/URN:NBN:fi:aalto-202308114749
    DOI: 10.1109/JSYST.2023.3286329 View at publisher
  • [Publication 3]: Wencan Mao, Ozgur Umut Akgul, Byungjin Cho, Yu Xiao, and Antti Ylä-Jääski. On-demand Vehicular Fog Computing for Beyond 5G Networks. Accepted for publication in IEEE Transactions on Vehicular Technology, 1-17 pages, June 2023. August 2022
  • [Publication 4]: Wencan Mao, Jiaming Yin, Yushan Liu, Byungjin Cho, Yang Chen,Weixiong Rao, and Yu Xiao. Multi-agent Reinforcement Learning-based Capacity Planning for On-demand Vehicular Fog Computing. Submitted to pre-review, June 2023. August 2022
Citation