DEEP LEARNING-DRIVEN DIFFERENTIATED TRAFFIC SCHEDULING IN CLOUD-IOT DATA CENTER NETWORKS

Xianju Wang (), Tao Chen (), Shuguang Chen, Yong Zhu (), Junhao Liu (), Jingxiu Xu (), Samaneh Soradi-Zeid () and Amin Yousefpour ()
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Xianju Wang: School of Physics and Electronic Engineering, Fuyang Normal University, Fuyang 236000, P. R. China
Tao Chen: School of Physics and Electronic Engineering, Fuyang Normal University, Fuyang 236000, P. R. China
Shuguang Chen: School of Physics and Electronic Engineering, Fuyang Normal University, Fuyang 236000, P. R. China†Agricultural Products Quality Safety Digital Intelligent, Engineering Research Center of Anhui, Fuyang 236037, P. R. China
Yong Zhu: School of Physics and Electronic Engineering, Fuyang Normal University, Fuyang 236000, P. R. China
Junhao Liu: School of Physics and Electronic Engineering, Fuyang Normal University, Fuyang 236000, P. R. China
Jingxiu Xu: ��School of Computer Science and Technology, Huanggang Normal University, HuangGang 438000, P. R. China
Samaneh Soradi-Zeid: �Faculty of Industry and Mining (Khash), University of Sistan and Baluchestan, Zahedan 9816745845, Iran
Amin Yousefpour: �Department of Mechanical and Aerospace Engineering, University of California, Irvine, CA 94720, USA

FRACTALS (fractals), 2023, vol. 31, issue 06, 1-14

Abstract: The development of 5G technology has enabled the cloud-internet of things (IoT) to impact all areas of our lives. Sensors in cloud-IoT generate large-scale data, and the demand for massive data processing is also increasing. The performance of a single machine can no longer meet the needs of existing users. In contrast, a data center (DC) integrates computing power and storage resources through a specific network topology and satisfies the need to process massive data. Regarding large-scale heterogeneous traffic in DCs, differentiated traffic scheduling on demand reduces transmission latency and improves throughput. Therefore, this paper presents a traffic scheduling method based on deep Q-networks (DQN). This method collects network parameters, delivers them to the environment module, and completes the environment construction of network information and reinforcement learning elements through the environment module. Thus, the final transmission path of the elephant flow is converted based on the action given by DQN. The experimental results show that the method proposed in this paper effectively reduces the transmission latency and improves the link utilization and throughput to a certain extent.

Keywords: Cloud-IoT; Data Center Networks; Differentiated Traffic Scheduling; Deep Learning; Elephant Flow (search for similar items in EconPapers)
Date: 2023
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DOI: 10.1142/S0218348X2340145X

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