Delay-Aware Resource Allocation in Fog-Assisted IoT Networks Through Reinforcement Learning

被引:22
作者
Fan, Qiang [1 ]
Bai, Jianan [1 ]
Zhang, Hongxia [1 ,2 ]
Yi, Yang [1 ]
Liu, Lingjia [1 ]
机构
[1] Virginia Tech, Bradley Dept Elect & Comp Engn, Blacksburg, VA 24060 USA
[2] China Univ Petr, Coll Comp Sci & Technol, Qiangdao 266580, Peoples R China
基金
美国国家科学基金会;
关键词
Task analysis; Resource management; Delays; Internet of Things; Cloud computing; Wireless communication; Reinforcement learning; Edge computing; fog computing; machine learning; online algorithm; reinforcement learning; resource allocation; POWER; SYSTEMS;
D O I
10.1109/JIOT.2021.3111079
中图分类号
TP [自动化技术、计算机技术];
学科分类号
0812 ;
摘要
Fog nodes in the vicinity of IoT devices are promising to provision low-latency services by offloading tasks from IoT devices to them. Mobile IoT is composed by mobile IoT devices, such as vehicles, wearable devices, and smartphones. Owing to the time-varying channel conditions, traffic loads, and computing loads, it is challenging to improve the Quality of Service (QoS) of mobile IoT devices. As task delay consists of both the transmission delay and computing delay, we investigate the resource allocation (i.e., including both radio resource and computation resource) in both the wireless channel and fog node to minimize the delay of all tasks while their QoS constraints are satisfied. We formulate the resource allocation problem into an integer nonlinear problem, where both the radio resource and computation resource are taken into account. As IoT tasks are dynamic, the resource allocation for different tasks are coupled with each other and the future information is impractical to be obtained. Therefore, we design an online reinforcement learning algorithm to make the suboptimal decision in real time based on the system's experience replay data. The performance of the designed algorithm has been demonstrated by extensive simulation results.
引用
收藏
页码:5189 / 5199
页数:11
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