Please use this identifier to cite or link to this item:
https://rda.sliit.lk/handle/123456789/2651
Title: | Network Traffic Prediction for a Software Defined Network Based Virtualized Security Functions Platform |
Authors: | Jayasinghe, D Rankothge, W. H Gamage, N. D. U Gamage, T. C. T Amarasinghe, D. A. H. M Uwanpriya, S. D. L. S |
Keywords: | Network Traffic Traffic Prediction Software Defined Network Network Based Security Functions Platform Virtualized |
Issue Date: | 6-Dec-2021 |
Publisher: | IEEE |
Citation: | D. Jayasinghe, W. H. Rankothge, N. D. U. Gamage, T. C. T. Gamage, S. D. L. S. Uwanpriya and D. A. H. M. Amarasinghe, "Network Traffic Prediction for a Software Defined Network Based Virtualized Security Functions Platform," 2021 IEEE 12th Annual Information Technology, Electronics and Mobile Communication Conference (IEMCON), 2021, pp. 1083-1088, doi: 10.1109/IEMCON53756.2021.9623169. |
Series/Report no.: | 2021 IEEE 12th Annual Information Technology, Electronics and Mobile Communication Conference (IEMCON); |
Abstract: | Software-Defined Networking (SDN) has become a popular and widely used approach with Cloud Service Providers (CSPs). With the introduction of Virtualized Security Functions (VSFs), and offering them as a service, CSPs are exploring effective and efficient approaches for resource management in the cloud infrastructure, considering specific requirements of VSFs. Network traffic prediction is an important component of cloud resource management, as prediction helps CSPs to take necessary proactive management actions, specifically for VSFs. This research focuses on introducing an algorithm to predict the network traffic traverse via a cloud platform where VSFs are offered as a service, by using the Auto-Regressive Integrated Moving Average (ARIMA) model. In this paper, the implementation and performance of the traffic prediction algorithm are presented. The results show that the network traffic in cloud environments can be effectively predicted by using the introduced algorithm with an accuracy of 96.49%. |
URI: | http://rda.sliit.lk/handle/123456789/2651 |
ISSN: | 2644-3163 |
Appears in Collections: | Department of Computer Science and Software Engineering-Scopes Department of Computer systems Engineering-Scopes Research Papers - Dept of Computer Systems Engineering Research Papers - IEEE Research Papers - SLIIT Staff Publications |
Files in This Item:
File | Description | Size | Format | |
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Network_Traffic_Prediction_for_a_Software_Defined_Network_Based_Virtualized_Security_Functions_Platform.pdf Until 2050-12-31 | 1.01 MB | Adobe PDF | View/Open Request a copy |
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