Task Migration and Resource Allocation Scheme in IoV With Roadside Unit
Zhuofan Liao, Shuangle Xu, Jiawei Huang, Jianxin Wang
Abstract
Mobile Edge Computing (MEC) has attracted attention for its short-range and low-latency computing services for the Internet of Vehicles (IoV). However, in the Vehicle-RSU-MEC environment, incomplete task migration is a problem when Vehicle Users(VUs) are moving at high speeds around the Road Side Units (RSUs). Additionally, during peak traffic periods or road congestion, completed tasks compete for MEC server computing resources, leading to load imbalance. Therefore, making reasonable migration and offloading decisions is an important challenge. To address this challenge, the paper proposes a Cooperative Offloading strategy to jointly optimize offloading Decisions and Allocation of computing resources (CODA) step by step. First, to solve the problem of incomplete task migration, CODA proposed a Greedy-Based Task Completion Migration (GBTCM) algorithm. The algorithm calculates the required RSU set for each task to achieve complete migration, and greedily searches for the optimal migration target in the corresponding set to reduce task transmission latency. Second, after completing the task migration, CODA proposed a Distance-Based Computing Resource Allocation (DBCRA) algorithm to achieve load balancing for MEC servers. The algorithm prioritizes distance and finds MEC servers with sufficient computing resources to achieve better load balancing performance. Experimental results have shown that CODA is a low-complexity algorithm applicable to IoV, which can make reasonable and rapid decisions for task migration and offloading. Compared to three other benchmarks, CODA exhibits higher effectiveness and superiority.