A Deep RL Approach on Task Placement and Scaling of Edge Resources for Cellular Vehicle-to-Network Service Provisioning
This addresses the challenge of efficiently provisioning real-time C-V2N services for vehicles and networks, representing an incremental improvement with a novel method for a known bottleneck.
The paper tackles the interdependent problems of service task placement and scaling of edge resources for Cellular Vehicle-to-Network (C-V2N) services, proposing a Deep Hybrid Policy Gradient (DHPG) approach that outperforms state-of-the-art solutions by guaranteeing the 99th percentile of service delay target while optimizing resource utilization.
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 $99^{th}$ 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.