A Distributed Intelligence Framework for Microservice-Oriented Task Offloading and Resource Allocation in Vehicular Edge-Cloud Networks

The increasing number of connected vehicles and intelligent transportation systems has created a significant need for efficient offloading and resource management in vehicular networks. These networks face challenges such as high mobility, variable network conditions, and diverse resource types. Traditional centralized methods cannot handle these issues effectively. Scalable and decentralized solutions are necessary to reduce latency, energy use, and computational overhead while maintaining reliable task execution. This research introduces a Decentralized PDE-Guided Microservice Offloading and Resource Allocation Framework. It uses a Multi-Agent Deep Reinforcement Learning (DRL) approach. The system is modeled as a Decentralized Partially Observable Markov Decision Process (Dec-POMDP). This allows vehicles and fog nodes to make localized decisions. We model vehicle density as Partial Differential Equations (PDEs) and Directed Acyclic Graphs (DAGs) to represent microservices at a fine-grained level. The framework applies Multi-Agent Proximal Policy Optimization (MAPPO) to optimize task splitting, offloading, and routing. Simulation results show the framework’s strong performance. Compared to baseline methods, it achieves up to 21.14% lower task completion time, 23.73% energy savings, and 17.75% reduced offloading latency.