Joint optimization of offloading strategy and resource allocation for multi-user in dynamic vehicular edge computing systems

IF 4.3 3区 材料科学 Q1 ENGINEERING, ELECTRICAL & ELECTRONIC ACS Applied Electronic Materials Pub Date : 2024-07-20 DOI:10.1016/j.simpat.2024.103001
Zhuocheng Du , Yuanzhi Ni , Hongfeng Tao , Mingfeng Yin
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Abstract

Internet of Vehicles (IoV) relies heavily on its computing capability to facilitate various vehicular applications. Since the cloud computing or mobile edge computing (MEC) only cannot satisfy the latency requirement due to the limitation of the resource coverage, the cloud–edge-end cooperative computing has become an emerging paradigm. A comprehensive IoV architecture is considered and a joint optimization problem is formulated to minimize the system function value. To optimize the resource allocation and the task offloading strategy, the simulated spring system algorithm (SSSA) is designed where the initial problem is decoupled into two sub-problems with priority. The first one is to allocate computing resources based on KKT conditions, thus the individual optimal solution is achieved. The second one is solved based on the idea of simulated spring system such that the task offloading strategy is obtained. Two sub-problems iterate mutually to update each other until finishing the binary tree traversal. Thus, the proposed solution adapts to various conditions and the computational complexity is also reduced compared with traditional methods. Simulation verifies that the proposed algorithm reduces the maximum system function value by about 31% compared with the benchmark methods and performs efficiently in various road conditions.

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动态车载边缘计算系统中多用户卸载策略和资源分配的联合优化
车联网(IoV)在很大程度上依赖其计算能力来促进各种车辆应用。由于资源覆盖范围的限制,仅靠云计算或移动边缘计算(MEC)无法满足延迟要求,因此云-边-端协同计算已成为一种新兴模式。本文考虑了一种全面的 IoV 架构,并提出了一个联合优化问题,以最小化系统函数值。为了优化资源分配和任务卸载策略,设计了模拟弹簧系统算法(SSSA),将初始问题解耦为两个具有优先级的子问题。第一个子问题是根据 KKT 条件分配计算资源,从而实现单个最优解。第二个子问题基于模拟弹簧系统的思想进行求解,从而获得任务卸载策略。两个子问题相互迭代更新,直到完成二叉树遍历。因此,与传统方法相比,所提出的解决方案能适应各种条件,计算复杂度也有所降低。模拟验证表明,与基准方法相比,所提出的算法将最大系统函数值降低了约 31%,并且在各种路况下都能高效执行。
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CiteScore
7.20
自引率
4.30%
发文量
567
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