Please use this identifier to cite or link to this item: https://rda.sliit.lk/handle/123456789/2651
Full metadata record
DC FieldValueLanguage
dc.contributor.authorJayasinghe, D-
dc.contributor.authorRankothge, W. H-
dc.contributor.authorGamage, N. D. U-
dc.contributor.authorGamage, T. C. T-
dc.contributor.authorAmarasinghe, D. A. H. M-
dc.contributor.authorUwanpriya, S. D. L. S-
dc.date.accessioned2022-06-21T07:00:13Z-
dc.date.available2022-06-21T07:00:13Z-
dc.date.issued2021-12-06-
dc.identifier.citationD. 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.en_US
dc.identifier.issn2644-3163-
dc.identifier.urihttp://rda.sliit.lk/handle/123456789/2651-
dc.description.abstractSoftware-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%.en_US
dc.language.isoenen_US
dc.publisherIEEEen_US
dc.relation.ispartofseries2021 IEEE 12th Annual Information Technology, Electronics and Mobile Communication Conference (IEMCON);-
dc.subjectNetwork Trafficen_US
dc.subjectTraffic Predictionen_US
dc.subjectSoftware Defined Networken_US
dc.subjectNetwork Baseden_US
dc.subjectSecurity Functionsen_US
dc.subjectPlatformen_US
dc.subjectVirtualizeden_US
dc.titleNetwork Traffic Prediction for a Software Defined Network Based Virtualized Security Functions Platformen_US
dc.typeArticleen_US
dc.identifier.doi10.1109/IEMCON53756.2021.9623169en_US
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 SizeFormat 
Network_Traffic_Prediction_for_a_Software_Defined_Network_Based_Virtualized_Security_Functions_Platform.pdf
  Until 2050-12-31
1.01 MBAdobe PDFView/Open Request a copy


Items in DSpace are protected by copyright, with all rights reserved, unless otherwise indicated.