A novel network virtualization based on data analytics in connected environment
- Authors
- Bui, Khac‑Hoai Nam; Cho, Sungrae; Jung, Jason J.; Kim, Joong Heon; Lee, O‑Joun; Na, Woongsoo
- Issue Date
- Jan-2020
- Publisher
- Springer Verlag
- Keywords
- Big data analytics; Connected environment; Data-driven networking; Heterogeneous network; Machine learning techniques; Network interference; Network virtualization
- Citation
- Journal of Ambient Intelligence and Humanized Computing, v.11, no.1(SI), pp 75 - 86
- Pages
- 12
- Journal Title
- Journal of Ambient Intelligence and Humanized Computing
- Volume
- 11
- Number
- 1(SI)
- Start Page
- 75
- End Page
- 86
- URI
- https://scholarworks.bwise.kr/cau/handle/2019.sw.cau/3327
- DOI
- 10.1007/s12652-018-1083-x
- ISSN
- 1868-5137
1868-5145
- Abstract
- Big data analytics is a growing trend for network and service management. Some approaches such as statistical analysis, data mining and machine learning have become promising techniques to improve operations and management of information technology systems and networks. In this paper, we introduce a novel approach for network management in terms of abnormality detection based on data analytics. Particularly, the main research focuses on how the network configuration can be automatically and adaptively decided, given various dynamic contexts (e.g., network interference, heterogeneity and so on). Specifically, we design a context-based data-driven framework for network operation in connected environment which includes three layer architecture: (i) network entity layer; (ii) complex semantic analytics layer and (iii) action provisioning layer. A case study on interference-based abnormal detection for connected vehicle explains more detail about our work. © 2018, Springer-Verlag GmbH Germany, part of Springer Nature.
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Collections - College of Software > School of Computer Science and Engineering > 1. Journal Articles
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