Task Allocation and Resource Scheduling in Vehicular Fog Computing

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Journal Title
Journal ISSN
Volume Title
School of Science | Doctoral thesis (article-based) | Defence date: 2020-12-23
Date
2020
Major/Subject
Mcode
Degree programme
Language
en
Pages
65 + app. 59
Series
Aalto University publication series DOCTORAL DISSERTATIONS, 216/2020
Abstract
Future vehicles will become smarter and more connected. With the merging and sharing of data generated by onboard sensors, next-generation vehicular applications (e.g., cooperative lane change) are emerging that create a significant demand for sufficient computing resources to conduct time-critical and data-intensive tasks. Owing to the space, weight, and cost constraints, the computing capacity of most vehicles may not be high enough to handle such tasks. On the other hand, offloading these tasks to the cloud is not applicable, due to the considerable transmission delay. A new computing paradigm known as vehicular fog computing (VFC) has been proposed that pushes computing and communication resources to the edge of the network. Its key idea is to offload computational tasks from the client vehicles to fog nodes located for example at cellular base stations or buses with extra computing power. In VFC, only one-hop communication is required, which greatly shortens the transmission delay. However, due to the mobility of vehicles, the density of client vehicles and therefore the amount of tasks generated by them vary spatiotemporally. Meanwhile, the availability of fog nodes carried by vehicles, called vehicular fog nodes, depends on the driving routes of the carriers. The spatiotemporal variation in both the supply and demand of computing services adds a layer of complexity to enable reliable VFC-based services. In this dissertation, the focus is on designing task allocation and resource scheduling algorithms via various mathematical models to enable high-quality and low-latency VFC-based services for vehicular applications. The feasibility and challenges of applying VFC for real-time analytics of a crowdsourced dash camera video is investigated. Furthermore, a framework for latency and quality optimized task allocation in VFC is presented and a task-offloading framework for visual-based assisted driving is proposed. Finally, the design of a QoI and latency aware task allocation scheme for vehicle-based visual crowdsourcing is presented, which takes into account vehicle mobility and the spatiotemporal variation in the workload of vehicular fog nodes. Analytical studies were conducted in order to answer the research questions and evaluate the effectiveness of the designed algorithms, using real-world application profiles and traffic data as input.
Description
Supervising professor
Ylä-Jääski, Antti, prof., Aalto University, Department of Computer Science, Finland; Xiao, Yu, asst prof., Aalto University, Department of Communications and Networking, Finland
Thesis advisor
Pastor, Giancarlo, Aalto University, Department of Communications and Networking, Finland
Keywords
vehicular networks, edge/fog computing, task allocation, crowdsourcing
Other note
Parts
  • [Publication 1]: Chao Zhu, Giancarlo Pastor, Yu Xiao, and Antti Ylä-Jääski. Vehicular Fog Computing for Video Crowdsourcing: Applications, Feasibility, and Challenges. IEEE Communications Magazine, vol.56, no.10, pages 58-63, Oct. 2018.
    Full text in Acris/Aaltodoc: http://urn.fi/URN:NBN:fi:aalto-201811095657
    DOI: 10.1109/MCOM.2018.1800116 View at publisher
  • [Publication 2]: Chao Zhu, Jin Tao, Giancarlo Pastor, Yu Xiao, Yusheng Ji, Quan Zhou, Yong Li, and Antti Ylä-Jääski. Folo: Latency and Quality Optimized Task Allocation in Vehicular Fog Computing. IEEE Internet of Things Journal, vol.6, no.3, pages 4150-4161, Oct. 2018.
    Full text in Acris/Aaltodoc: http://urn.fi/URN:NBN:fi:aalto-201810245529
    DOI: 10.1109/JIOT.2018.2875520 View at publisher
  • [Publication 3]: Chao Zhu, Yi-Han Chiang, Abbas Mehrabi, Yu Xiao, Antti Ylä-Jääski, and Yusheng Ji. Chameleon: Latency and Resolution Aware Task Offloading for Visual-based Assisted Driving. IEEE Transactions on Vehicular Technology, vol.68, no.9, pages 9038-9048, Jul. 2019. .
    Full text in Acris/Aaltodoc: http://urn.fi/URN:NBN:fi:aalto-201911076164
    DOI: 10.1109/TVT.2019.2924911 View at publisher
  • [Publication 4]: Chao Zhu, Yi-Han Chiang, Yu Xiao, and Yusheng Ji. FlexSensing: A QoI and Latency Aware Task Allocation Scheme for Vehicle-based Visual Crowdsourcing via Deep Q-Network. IEEE Internet of Things Journal, Accepted for Publication, Nov. 2020
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