协同移动边缘计算分层资源配置机制

针对车联网（IoV）中存在大量的车辆卸载任务计算需求，而本地端边缘服务器运算能力有限的问题，提出一种移动边缘计算分层协同资源配置机制（HRAM）。所提算法以多层式的架构合理分配与有效利用移动边缘计算（MEC）服务器的运算资源，减少不同MEC服务器之间的数据多跳转发时延，并优化卸载任务请求时延。首先构建IoV边缘计算系统模型、通信模型、决策模型和计算模型；然后利用层次分析法（AHP）进行多因素综合考虑以确定卸载任务迁移的目标服务器；最后提出动态权值的任务路由策略，调用整体网络的通信能力以缩短卸载任务的请求时延。仿真实验结果表明，HRAM算法相较于任务卸载单层式资源分配（RATAOS）算法和任务卸载多层式资源分配（RATOM）算法，分别降低了40.16%和19.01%的卸载任务请求时延；且所提算法在满足卸载任务最大可容忍时延的前提下，能够满足更多卸载任务的计算需求。

Hierarchical resource allocation mechanism of cooperative mobile edge computing

Concerning the large number of computing needs of vehicle task offloading and the limited computing capacity of local edge servers in the Internet of Vehicles （IoV）， a Hierarchical Resource Allocation Mechanism of cooperative mobile edge computing （HRAM） was proposed. In this algorithm， the computing resources of Mobile Edge Computing （MEC） servers were reasonably allocated and effectively utilized with a multi-layer architecture，so that the data multi-hop forwarding delay between different MEC servers was reduced， and the delay of task offloading requests was optimized. Firstly， the system model， communication model， decision model， and calculation model of the IoV edge computing were built. Next， the Analytic Hierarchy Process （AHP） was used to comprehensively consider multiple factors to determine the target server the offloaded task transferred to. Finally， a task routing strategy with dynamic weights was proposed to make use of communication capabilities of the overall network to shorten the request delay of task offloading. Simulation results show that compared with Resource Allocation of Task Offloading in Single-hop （RATOS） algorithm and Resource Allocation of Task Offloading in Multi-hop （RATOM） algorithm， HRAM algorithm reduces the request delay of task offloading by 40.16% and 19.01% respectively， and this algorithm can satisfy the computing needs of more offloaded tasks under the premise of meeting the maximum tolerable delay.