# A Deep RL Approach on Task Placement and Scaling of Edge Resources for Cellular Vehicle-to-Network Service Provisioning

arXiv: https://arxiv.org/abs/2305.09832 · HTML: https://mlsanity.com/paper/2305.09832/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 19 of its 108 references that are in the map.
- First posted on arXiv 16 May 2023.

## Builds on (most cited first, 19 of 19)

- [Adam: A Method for Stochastic Optimization](https://mlsanity.com/paper/1412.6980/) (2014)
- [Continuous control with deep reinforcement learning](https://mlsanity.com/paper/1509.02971/) (2015)
- [Playing Atari with Deep Reinforcement Learning](https://mlsanity.com/paper/1312.5602/) (2013)
- [Addressing Function Approximation Error in Actor-Critic Methods](https://mlsanity.com/paper/1802.09477/) (2018)
- [Fast and Accurate Deep Network Learning by Exponential Linear Units (ELUs)](https://mlsanity.com/paper/1511.07289/) (2015)
- [Understanding O-RAN: Architecture, Interfaces, Algorithms, Security, and Research Challenges](https://mlsanity.com/paper/2202.01032/) (2022)
- [A Tutorial on 5G NR V2X Communications](https://mlsanity.com/paper/2102.04538/) (2021)
- [Deep Reinforcement Learning for Resource Management in Network Slicing](https://mlsanity.com/paper/1805.06591/) (2018)
- [6G for Vehicle-to-Everything (V2X) Communications: Enabling Technologies, Challenges, and Opportunities](https://mlsanity.com/paper/2012.07753/) (2020)
- [Decentralized Computation Offloading for Multi-User Mobile Edge Computing: A Deep Reinforcement Learning Approach](https://mlsanity.com/paper/1812.07394/) (2018)
- [Performance Optimization in Mobile-Edge Computing via Deep Reinforcement Learning](https://mlsanity.com/paper/1804.00514/) (2018)
- [End-to-End V2X Latency Modeling and Analysis in 5G Networks](https://mlsanity.com/paper/2201.06082/) (2022)
- [π-ROAD: a Learn-as-You-Go Framework for On-Demand Emergency Slices in V2X Scenarios](https://mlsanity.com/paper/2012.06208/) (2020)
- [Resource Allocation for Edge Computing in IoT Networks via Reinforcement Learning](https://mlsanity.com/paper/1903.01856/) (2019)
- [Computation Offloading for IoT in C-RAN: Optimization and Deep Learning](https://mlsanity.com/paper/1909.10696/) (2019)
- [BARGAIN-MATCH: A Game Theoretical Approach for Resource Allocation and Task Offloading in Vehicular Edge Computing Networks](https://mlsanity.com/paper/2203.14064/) (2022)
- [Reducing Service Deployment Cost Through VNF Sharing](https://mlsanity.com/paper/1910.03611/) (2019)
- [Near Optimal VNF Placement in Edge-Enabled 6G Networks](https://mlsanity.com/paper/2111.07849/) (2021)
- [V2N Service Scaling with Deep Reinforcement Learning](https://mlsanity.com/paper/2301.13324/) (2023)

## Abstract (arXiv)

Cellular Vehicle-to-Everything (C-V2X) is currently at the forefront of the digital transformation of our society. By enabling vehicles to communicate with each other and with the traffic environment using cellular networks, we redefine transportation, improving road safety and transportation services, increasing efficiency of vehicular traffic flows, and reducing environmental impact. To effectively facilitate the provisioning of Cellular Vehicular-to-Network (C-V2N) services, we tackle the interdependent problems of service task placement and scaling of edge resources. Specifically, we formulate the joint problem and prove that it is not computationally tractable. To address its complexity we propose Deep Hybrid Policy Gradient (DHPG), a new Deep Reinforcement Learning (DRL) approach that operates in hybrid action spaces, enabling holistic decision-making and enhancing overall performance. We evaluated the performance of DHPG using simulations with a real-world C-V2N traffic dataset, comparing it to several state-of-the-art (SoA) solutions. DHPG outperforms these solutions, guaranteeing the 99th percentile of C-V2N service delay target, while simultaneously optimizing the utilization of computing resources. Finally, time complexity analysis is conducted to verify that the proposed approach can support real-time C-V2N services.