Literature Database Entry

yan2025reinforcement

Christopher Ankai Yan, "Reinforcement Learning for Task Scheduling in Vehicular Micro Clouds," Bachelor Thesis, School of Electrical Engineering and Computer Science (EECS), TU Berlin (TUB), February 2025. (Advisor: Agon Memedi; Referees: Falko Dressler and Thomas Sikora)

Abstract

As demand for computational power rises with new applications such as blockchain technology and machine learning, innovative solutions are needed to meet growing processing demands. This thesis explores the use of Vehicular Mirco Clouds (VMCs), in which vehicles with onboard computers form a cluster to provide task computation. A key challenge of task processing within a VMC is its dynamic nature, as vehicles only have a limited VMC dwell time. This requires efficient task-to-vehicle assignments that ensure completion of scheduled tasks before vehicle departures, while maximizing total completed tasks and adhering to task deadline constraints. Reinforcement Learning has shown significant results in handling complex and dynamic environments. This research applies Deep Q-Learning (DQL), a Reinforcement Learning algorithm, to the VMC task scheduling problem. Two DQL agents were trained in fixed and dynamic VMC environments of varying complexity. The scheduling performance of the two agents was evaluated against three benchmark scheduling policies: Earliest Deadline First (EDF), Lowest Complexity First (LCF) and Random, for two VMC traffic densities (low and high). Although both agents demonstrated limited training progress, the agent trained on the dynamic VMC displayed improved performance over the fixed VMC agent. Additionally, the dynamic VMC agent partly outperformed the Random policy in low and high VMC density, achieving fewer mean task interruptions and reducing mean task processing time by approximately 0.4 time units. Future research on tuning DQL and VMC parameters could provide deeper insights and improve scheduling efficiency.

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Christopher Ankai Yan

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@phdthesis{yan2025reinforcement,
    author = {Yan, Christopher Ankai},
    title = {{Reinforcement Learning for Task Scheduling in Vehicular Micro Clouds}},
    advisor = {Memedi, Agon},
    institution = {School of Electrical Engineering and Computer Science (EECS)},
    location = {Berlin, Germany},
    month = {2},
    referee = {Dressler, Falko and Sikora, Thomas},
    school = {TU Berlin (TUB)},
    type = {Bachelor Thesis},
    year = {2025},
   }
   
   

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Last modified: 2026-04-30