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

ABSTRACT 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 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.