Zhu, Shuya; Meng, Kaitao; Wang, Rui; Li, Deshi; (2024) Coordinated Computing Resource Allocation With Efficiency Maximization in Heterogeneous Platoon Edge Network. IEEE Transactions on Intelligent Transportation Systems 10.1109/tits.2024.3435760. (In press).

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Abstract

Under numerous computation requirements in intelligent traffic, platoons comprised of several connected vehicles are expected to centralize vehicular computation resources, which can provide computing services for surrounding mobile users and thus facilitate the deployment of vehicular edge computing (VEC). Nevertheless, the computing resources in multi-platoon are distributed unevenly, making platoons’ resource allocation highly selective, especially for randomly distributed mobile users. Moreover, existing works mainly focus on single-platoon-assisted VEC, which lacks resource coordination among platoons and may result in resource imbalance. Thus, through coordinating computing resource allocation in platoons and base stations (BSs), a coordinated computing resource allocation scheme is proposed in this paper to maximize the utilities of mobile users. However, the formulated problem is a mixed integer nonlinear programming (MINLP) problem, which is NP-hard. To address this issue, the efficiency-maximized optimal computing resources allocated from platoons to users are derived in a semi-closed form. Then, to decouple the variables in allocation coordination, the efficiency maximization problem is proven to be equivalent to a low-complexity resource allocation problem oriented for single users. Based on the above results, two algorithms are proposed to convert the NP-hard problem into convex ones, 1) through efficiency-maximized task offloading and backtracking iteratively, a profit efficiency backtracking algorithm is proposed to coordinate computing resource allocation among platoons, and 2) heterogeneous profit efficiency algorithm is proposed to solve the primal problem, where a partition ratio-based efficiency maximization computing resource allocation problem is optimized in heterogeneous edges. Extensive simulation results show that our proposed scheme can improve resource allocation efficiency, task success rates, and service continuity over benchmark schemes.

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