Literature Database Entry

memedi2025simulator

Agon Memedi, Chunghan Lee, Seyhan Ucar, Onur Altintas and Falko Dressler, "Simulator for Reinforcement Learning-based Resource Management in Vehicular Edge Computing," Proceedings of 16th IEEE Vehicular Networking Conference (VNC 2025), Poster Session, Porto, Portugal, June 2025. (to appear)

Abstract

We aim to investigate reinforcement learning (RL) methods for efficient resource management in vehicular edge computing (VEC). To this end, we present an open-source, modular, lightweight, discrete-event simulation framework which integrates state-of-the-art tools for improved performance evaluation. By integrating realistic mobility traces, our approach presents an opportunity to evaluate the performance and scalability of different RL-based task scheduling and resource allocation policies in diverse scenarios. This offers flexibility and insights into the generalizability of RL-based scheduling policies. We make the framework available as open-source to foster broader accessibility, support research in the field. We present early results to demonstrate the potential of this simulator.

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Agon Memedi
Chunghan Lee
Seyhan Ucar
Onur Altintas
Falko Dressler

BibTeX reference

@inproceedings{memedi2025simulator,
    author = {Memedi, Agon and Lee, Chunghan and Ucar, Seyhan and Altintas, Onur and Dressler, Falko},
    note = {to appear},
    title = {{Simulator for Reinforcement Learning-based Resource Management in Vehicular Edge Computing}},
    publisher = {IEEE},
    address = {Porto, Portugal},
    booktitle = {16th IEEE Vehicular Networking Conference (VNC 2025), Poster Session},
    month = {6},
    year = {2025},
   }
   
   

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