Mixed-Criticality Scheduling Toward Real-Time Applications in a Vehicular Edge Computing System

Scheduling applications in vehicular edge computing (VEC) systems poses significant challenges due to strict timing constraints and varying levels of criticality. This paper presents a three-stage scheduling framework designed to efficiently manage the execution of mixed-criticality applications. The proposed method introduces scheduling policies that reduce the complexity of scheduling dual-criticality DAG (Directed Acyclic Graph) applications on servers by transforming them into equivalent uniprocessor scheduling problems. To further enhance performance, a population-based evolutionary algorithm is employed to optimize virtual machine configurations on each server, while a game-theoretic approach assigns DAG applications to servers. Experimental results show that the proposed scheme outperforms both state-of-the-art dynamic programming (DP) and particle swarm optimization (PSO) methods. The proposed MCS approach achieves a strong balance between scheduling quality and computational efficiency, with an $\alpha$ of 0.87, an 80% success rate, and a low computation time (310 s), making it well-suited for real-time edge systems. Compared to other methods like PSO+, DP, and OneVM, MCS offers near-optimal performance while avoiding the high computational cost and scalability limitations faced by those alternatives.