Cuckoo Search-Enabled Task Scheduling and Cache Updating in Vehicular Edge-Fog Computing
Qiaoqiao Shen, Bin‐Jie Hu, Enjun Xia
Abstract
With the emergence of computation-intensive vehicle applications, vehicle edge-fog computing (VEFC) is playing an increasingly important role in intelligent transportation systems. In this article, we study the task scheduling and cache updating (TSCU) problem in VEFC, where vehicles can choose three modes to process their tasks: local computing, vehicle-to-vehicle (V2V) offloading, and vehicle-to-infrastructure (V2I) offloading. Service providers can dynamically update their caches to improve the system offloading efficiency. To maximize the offloading efficiency, we formulate the TSCU problem as a mixed-integer non-linear programming (MINLP) problem and develop an improved discrete cuckoo search algorithm to solve the optimization problem. Our proposed algorithm explores the global environment via Levy flight. On the other hand, the algorithm convergence rate is accelerated by generating the initial population based on a greedy algorithm and its variants and updating the discard probability. Simulation results show that our proposal has achieved an up to 12% improvement in offloading efficiency compared to the benchmark schemes.