The IRMA Community
Newsletters
Research IRM
Click a keyword to search titles using our InfoSci-OnDemand powered search:
|
A Decentralized PageRank Based Content Dissemination Model at the Edge of Network
|
|
Author(s): Xin Zhang (Institute of Acoustics, Chinese Academy of Sciences, University of Chinese Academy of Sciences, Huairou, China), Jiali You (Institute of Acoustics, Chinese Academy of Sciences, University of Chinese Academy of Sciences, Huairou, China), Hanxing Xue (Institute of Acoustics, Chinese Academy of Sciences, University of Chinese Academy of Sciences, Huairou, China)and Jinlin Wang (Institute of Acoustics, Chinese Academy of Sciences, University of Chinese Academy of Sciences, Huairou, China)
Copyright: 2020
Volume: 17
Issue: 1
Pages: 16
Source title:
International Journal of Web Services Research (IJWSR)
Editor(s)-in-Chief: Liang-Jie Zhang (Kingdee International Software Group, China)and Chia-Wen Tsai (Ming Chuan University, Taiwan)
DOI: 10.4018/IJWSR.2020010101
PurchaseView on the publisher's website for pricing and purchasing information.
|
Abstract
In the era of Internet of Things, cloud services are difficult to meet the real-time transmission requirements of users for the data generated in the edge of network especially for the Internet video services. Utilizing the devices at the edge of network, such as an intelligent router, to achieve nearby content services for users can effectively reduce backbone traffic and enhance service performance. This article proposes a decentralized PageRank-based content dissemination model at the edge of network, in which a suitable node selection algorithm is designed to distribute the content evenly in the network. Each node can quickly obtain data from neighbor nodes, thereby reducing the cloud load as well as the network bandwidth and improving the service response performance. The simulation shows that, compared with the other two dissemination algorithms, the content is distributed more even, which means every node has more opportunity to obtain the data from neighbors; and the service rejection rate can be decreased by an average of 5.2% in the case of high concurrent requests.
Related Content
|
Jinping Zhang.
© 2024.
17 pages.
|
|
Ahmad Radwan, Mohannad Amarneh, Hussam Alawneh, Huthaifa I. Ashqar, Anas AlSobeh, Aws Abed Al Raheem Magableh.
© 2024.
22 pages.
|
|
Zhuolin Mei, Huilai Zou, Jinzhou Huang, Caicai Zhang, Bin Wu, Jiaoli Shi, Zhengxiang Cheng.
© 2024.
17 pages.
|
|
Shouning Huang.
© 2024.
18 pages.
|
|
Xiang Xie, Jianxun Liu, Buqing Cao, Mi Peng, Guosheng Kang, Yiping Wen, Kenneth K. Fletcher.
© 2023.
17 pages.
|
|
Yunfei Li, Shichao Yin.
© 2023.
17 pages.
|
|
Yong Lu, Ming Zhe Jin.
© 2023.
14 pages.
|
|
|