# Cost-optimal V2X Service Placement in Distributed Cloud/Edge Environment

arXiv: https://arxiv.org/abs/2010.07223 · HTML: https://mlsanity.com/paper/2010.07223/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 14 Oct 2020.

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

- [DRLD-SP: A Deep Reinforcement Learning-based Dynamic Service Placement in Edge-Enabled Internet of Vehicles](https://mlsanity.com/paper/2106.06291/) (2021)
- [Intelligent Transportation Systems' Orchestration: Lessons Learned & Potential Opportunities](https://mlsanity.com/paper/2205.14040/) (2022)

## Abstract (arXiv)

Deploying V2X services has become a challenging task. This is mainly due to the fact that such services have strict latency requirements. To meet these requirements, one potential solution is adopting mobile edge computing (MEC). However, this presents new challenges including how to find a cost efficient placement that meets other requirements such as latency. In this work, the problem of cost-optimal V2X service placement (CO-VSP) in a distributed cloud/edge environment is formulated. Additionally, a cost-focused delay-aware V2X service placement (DA-VSP) heuristic algorithm is proposed. Simulation results show that both CO-VSP model and DA-VSP algorithm guarantee the QoS requirements of all such services and illustrates the trade-off between latency and deployment cost.