# Reinforcement Learning-based Dynamic Service Placement in Vehicular Networks

arXiv: https://arxiv.org/abs/2105.15022 · HTML: https://mlsanity.com/paper/2105.15022/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Builds on 4 of its 17 references that are in the map.
- First posted on arXiv 31 May 2021.

## Builds on (most cited first, 4 of 4)

- [Deep Reinforcement Learning for Intelligent Transportation Systems: A Survey](https://mlsanity.com/paper/2005.00935/) (2020)
- [Survey on Multi-Access Edge Computing for Internet of Things Realization](https://mlsanity.com/paper/1805.06695/) (2018)
- [Edge-enabled V2X Service Placement for Intelligent Transportation Systems](https://mlsanity.com/paper/2001.06288/) (2020)
- [Toward Cloud-based Vehicular Networks with Efficient Resource Management](https://mlsanity.com/paper/1308.6208/) (2013)

## Built on by (most cited first, 2 of 2)

- [Adversarial Attacks Against Deep Reinforcement Learning Framework in Internet of Vehicles](https://mlsanity.com/paper/2108.00833/) (2021)
- [DRLD-SP: A Deep Reinforcement Learning-based Dynamic Service Placement in Edge-Enabled Internet of Vehicles](https://mlsanity.com/paper/2106.06291/) (2021)

## Abstract (arXiv)

The emergence of technologies such as 5G and mobile edge computing has enabled provisioning of different types of services with different resource and service requirements to the vehicles in a vehicular network.The growing complexity of traffic mobility patterns and dynamics in the requests for different types of services has made service placement a challenging task. A typical static placement solution is not effective as it does not consider the traffic mobility and service dynamics. In this paper, we propose a reinforcement learning-based dynamic (RL-Dynamic) service placement framework to find the optimal placement of services at the edge servers while considering the vehicle's mobility and dynamics in the requests for different types of services. We use SUMO and MATLAB to carry out simulation experiments. In our learning framework, for the decision module, we consider two alternative objective functions-minimizing delay and minimizing edge server utilization. We developed an ILP based problem formulation for the two objective functions. The experimental results show that 1) compared to static service placement, RL-based dynamic service placement achieves fair utilization of edge server resources and low service delay, and 2) compared to delay-optimized placement, server utilization optimized placement utilizes resources more effectively, achieving higher fairness with lower edge-server utilization.