Literature Database Entry

ji2026optimal

Guangchao Ji, "Optimal Task Allocation in Vehicular Environments for Virtualized Edge Computing," Master's Thesis, School of Electrical Engineering and Computer Science (EECS), TU Berlin (TUB), January 2026. (Advisor: Agon Memedi; Referees: Falko Dressler and Thomas Sikora)

Abstract

The rapid development of Intelligent and Connected Vehicles (ICVs) has led to an explosive increase in data generation and computational demand, straining the limited computing resources in conventional vehicular networks. To address this, Virtual Edge Computing (V-Edge) provides a solution by using Vehicular Micro Clouds (VMCs) to enable resource sharing via Vehicle-to-Vehicle (V2V) communication. While this approach offers a potential solution by offloading tasks within local vehicle clusters, the high mobility of vehicles and the dynamic nature of computational resources pose significant challenges to efficient task allocation. This thesis proposes a Deep Q Learning (DQL)-based framework to optimize task allocation within such VMCs. Unlike static heuristic methods, the proposed Deep Q-Network (DQN) agent learns adaptive scheduling policies by interacting with the environment. The framework is validated using the SUMO traffic simulator with the realistic Luxembourg SUMO Traffic (LuST) scenario. The evaluation compares the learned policy against heuristic baselines, including Random, Earliest Deadline First (EDF), and Lowest Complexity First (LCF), under both high-load and low-load settings. Experimental results show that under high traffic load, the DQN agent achieves a 55.2% task success rate, improving on the random policy by 8.4 percentage points. The Empirical Cumulative Distribution Function (ECDF) of utilization further indicates that this gain is achieved with a more balanced workload distribution across vehicles than Random. Under low load, the learned policy still achieves the highest success rate, supporting efficient scheduling in dynamic urban environments.

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Guangchao Ji

BibTeX reference

@phdthesis{ji2026optimal,
    author = {Ji, Guangchao},
    title = {{Optimal Task Allocation in Vehicular Environments for Virtualized Edge Computing}},
    advisor = {Memedi, Agon},
    institution = {School of Electrical Engineering and Computer Science (EECS)},
    location = {Berlin, Germany},
    month = {1},
    referee = {Dressler, Falko and Sikora, Thomas},
    school = {TU Berlin (TUB)},
    type = {Master's Thesis},
    year = {2026},
   }
   
   

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Last modified: 2026-04-20