Dynamic Computation Offloading with Deep Reinforcement Learning in Edge Network

被引:1
|
作者
Bai, Yang [1 ]
Li, Xiaocui [1 ]
Wu, Xinfan [1 ]
Zhou, Zhangbing [1 ,2 ]
机构
[1] China Univ Geosci Beijing, Sch Informat Engn, Beijing 100083, Peoples R China
[2] TELECOM SudParis, Comp Sci Dept, F-91000 Evry, France
来源
APPLIED SCIENCES-BASEL | 2023年 / 13卷 / 03期
基金
中国国家自然科学基金;
关键词
computation offloading; service migration; deep reinforcement learning; SERVICE MIGRATION; ENERGY; OPTIMIZATION; AWARE;
D O I
10.3390/app13032010
中图分类号
O6 [化学];
学科分类号
0703 ;
摘要
With the booming proliferation of user requests in the Internet of Things (IoT) network, Edge Computing (EC) is emerging as a promising paradigm for the provision of flexible and reliable services. Considering the resource constraints of IoT devices, for some delay-aware user requests, a heavy-workload IoT device may not respond on time. EC has sparked a popular wave of offloading user requests to edge servers at the edge of the network. The orchestration of user-requested offloading schemes creates a remarkable challenge regarding the delay in user requests and the energy consumption of IoT devices in edge networks. To solve this challenge, we propose a dynamic computation offloading strategy consisting of the following: (i) we propose the concept of intermediate nodes, which can minimize the delay in user requests and the energy consumption of the current tasks handled by IoT devices by dynamically combining task-offloading and service migration strategies; (ii) based on the workload of the current network, the intermediate node selection problem is modeled as a multi-dimensional Markov Decision Process (MDP) space, and a deep reinforcement learning algorithm is implemented to reduce the large MDP space and make a fast decision. Experimental results show that this strategy is superior to the existing baseline methods to reduce delays in user requests and the energy consumption of IoT devices.
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页数:20
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