Dynamic Scaling Mechanism of IoV Microservices Based on Traffic Flow Prediction and Deep Reinforcement Learning

With the deep integration of Internet of Vehicles (IoV) and edge computing technologies, the spatiotemporal dynamics, burstiness, and load fluctuations of user requests pose severe challenges to microservices auto-scaling. Existing static or periodic resource adjustment strategies struggle to adapt to IoV edge environments and often neglect service dependencies and multi-objective optimization synergy, failing to fully utilize implicit regularities like the symmetry in spatiotemporal patterns. This paper proposes a dual-phase dynamic scaling mechanism: for long-term scaling, the Spatio-Temporal Graph Transformer (STGT) is employed to predict traffic flow by capturing correlations in spatial–temporal distributions of vehicle movements, and the improved Multi-objective Graph-based Proximal Policy Optimization (MGPPO) algorithm is applied for proactive resource optimization, balancing trade-offs among conflicting objectives. For short-term bursts, the Fast Load-Aware Auto-Scaling algorithm (FLA) enables rapid instance adjustment based on the M/M/S queuing model, maintaining balanced load distribution across edge nodes—a feature that aligns with the principle of symmetry in system design. The model comprehensively considers request latency, resource consumption, and load balancing, using a multi-objective reward function to guide optimal strategies. Experiments show that STGT significantly improves prediction accuracy, while the combination of MGPPO and FLA reduces request latency and enhances resource utilization stability, validating its effectiveness in dynamic IoV environments.