Minimizing the Delay and Cost of Computation Offloading for Vehicular Edge Computing

• Published in: IEEE transactions on services computing Vol. 15; no. 5; pp. 2897 - 2909
• Main Authors: Luo, Quyuan, Li, Changle, Luan, Tom H., Shi, Weisong
• Format: Journal Article
• Language: English
• Published: Piscataway IEEE 01-09-2022; The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
• Subjects: Algorithms; Communication; Computation offloading; Computational modeling; Cost analysis; Decision theory; Delay; Delays; Edge computing; multi-objective optimization; Multiple objective analysis; Optimization; Pareto optimality; Pareto optimization; Pareto optimum; particle swarm; Particle swarm optimization; Resource management; Servers; Task analysis; Vehicles; Vehicular edge computing
• ISSN: 1939-1374; 1939-1374; 2372-0204
• DOI: 10.1109/TSC.2021.3064579

The development of autonomous driving poses significant demands on computing resource, which is challenging to resource-constrained vehicles. To alleviate the issue, Vehicular edge computing (VEC) has been developed to offload real-time computation tasks from vehicles. However, with multiple vehicles contending for the communication and computation resources at the same time for different applications, how to efficiently schedule the edge resources toward maximal system welfare represents a fundamental issue in VEC. This article aims to provide a detailed analysis on the delay and cost of computation offloading for VEC and minimize the delay and cost from the perspective of multi-objective optimization. Specifically, we first establish an offloading framework with communication and computation for VEC, where computation tasks with different requirements for computation capability are considered. To pursue a comprehensive performance improvement during computation offloading, we then formulate a multi-objective optimization problem to minimize both the delay and cost by jointly considering the offloading decision, allocation of communication and computation resources. By applying the game theoretic analysis, we propose a particle swarm optimization based computation offloading (PSOCO) algorithm to obtain the Pareto-optimal solutions to the multi-objective optimization problem. Extensive simulation results verify that our proposed PSOCO outperforms counterparts. Based on the results, we also present a comprehensive analysis and discussion on the relationship between delay and cost among the Pareto-optimal solutions.