IDEAS home Printed from https://ideas.repec.org/a/wsi/fracta/v31y2023i06ns0218348x2340145x.html
   My bibliography  Save this article

Deep Learning-Driven Differentiated Traffic Scheduling In Cloud-Iot Data Center Networks

Author

Listed:
  • 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)

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.

Suggested Citation

  • Xianju Wang & Tao Chen & Shuguang Chen & Yong Zhu & Junhao Liu & Jingxiu Xu & Samaneh Soradi-Zeid & Amin Yousefpour, 2023. "Deep Learning-Driven Differentiated Traffic Scheduling In Cloud-Iot Data Center Networks," FRACTALS (fractals), World Scientific Publishing Co. Pte. Ltd., vol. 31(06), pages 1-14.
    • Handle: RePEc:wsi:fracta:v:31:y:2023:i:06:n:s0218348x2340145x
    DOI: 10.1142/S0218348X2340145X
    as

    Download full text from publisher

    File URL: http://www.worldscientific.com/doi/abs/10.1142/S0218348X2340145X
    Download Restriction: Access to full text is restricted to subscribers
    File URL: https://libkey.io/10.1142/S0218348X2340145X?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    As the access to this document is restricted, you may want to search for a different version of it.

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:wsi:fracta:v:31:y:2023:i:06:n:s0218348x2340145x. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    We have no bibliographic references for this item. You can help adding them by using this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Tai Tone Lim (email available below). General contact details of provider: https://www.worldscientific.com/worldscinet/fractals .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.