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Joint Task Offloading and Resource Allocation for Vehicular Edge Computing With Result Feedback Delay
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DOI：10.1109/TWC.2023.3244391.png)
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摘要
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In this paper, we study the problem of joint Task offloading and resource Allocation for vehicular edge computing with Result Feedback Delay (TARFD). Specifically, we consider a typical roadside unit (RSU) and vehicles within its coverage area, and optimize the task offloading decisions of vehicles as well as the uplink bandwidth allocation and the computation resources allocation on the RSU. The TARFD problem is formulated as a non-convex mixed integer nonlinear programming (MINLP) to minimize the average delay consisting of task offloading delay, task computation delay, and result feedback delay. We derive a lower bound of the optimum to the TARFD problem, based on which we propose an approximate algorithm of the TARFD problem, called A-TARFD. The A-TARFD algorithm can effectively deliver solutions for small-scale scenarios. To tackle large-scale scenarios, a low-complexity algorithm for the TARFD problem, called L-TARFD, is developed by constructing an iteratively updated sequence of locally tight approximate geometric programming (GP) problems. The L-TARFD algorithm can converge to a Karush-Kuhn-Tucker (KKT) point and forces the offloading decisions arbitrarily close to binary values. By comparison with the lower bound, simulation results show that the proposed two algorithms have near-optimal performance over a wide range of parameter settings.
Keyword:
Vehicular edge computing
task offloading
resource allocation
result feedback
delay optimization
geometric programming
期刊
IF:
10.7
论文数:
1.3W
被引数:
5.3W
机构
引用论文
Joint Task Assignment and Resource Allocation for D2D-Enabled Mobile-Edge ComputingD2D-Enabled移动-边缘计算联合任务分配与资源分配
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