A Memetic Algorithm for Cooperative Complex Task Offloading in Heterogeneous Vehicular Networks

• Published in: IEEE transactions on network science and engineering Vol. 10; no. 1; pp. 189 - 204
• Main Authors: Ren, Hualing, Liu, Kai, Yan, Guozhi, Li, Yantao, Zhan, Choujun, Guo, Songtao
• Format: Journal Article
• Language: English
• Published: Piscataway IEEE 01.01.2023; The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
• Subjects: Algorithms; Cloud computing; complex task; Computation offloading; Computational modeling; Cooperative offloading; Delays; Edge computing; memetic computing; Memetics; Networks; online scheduling; Performance evaluation; Polynomials; Resource management; Servers; Strategy; Task analysis; Vehicles; vehicular network
• ISSN: 2327-4697; 2334-329X
• DOI: 10.1109/TNSE.2022.3206228

With the booming of intelligent connected vehicles as well as the emergence of edge computing paradigm, complex task offloading becomes a critical yet promising issue in vehicular networks to enable various real-time and scalable future intelligent systems. This article makes the first effort on proposing an end-edge-cloud cooperation architecture together with a tailored memetic algorithm for complex task offloading in heterogeneous vehicular networks. Specifically, we consider the scenario where a complex task consists of multiple subtasks, which require different amount of resources for being processed, and a task is completed only when all of its subtasks have been processed. Then, we formulate a Cooperative Complex Task Offloading (CCTO) problem by considering heterogeneous computation, communication and memory capacities of nodes in vehicular networks, as well as task dependency and mobility of vehicles, targeting at minimizing average service delay of the system. We prove that CCTO is NP-hard by constructing a polynomial time reduction from the parallel machines scheduling problem (PMSP). Further, we propose a memetic computing based algorithm named MDMA (Meme Dependency-aware Memetic Algorithm), which consists of a meme dependency based encoding solution, a mix-strategy for initialization, a dedicated offspring generation scheme, a meme recombined strategy for local search, and a task feature driven method for repairing infeasible solutions. Finally, we build a simulation model and give a comprehensive performance evaluation. The results demonstrate the superiority of MDMA on minimizing the service delay by best exploiting heterogeneous resources in vehicular networks.