Research Papers - Dept of Computer Systems Engineering

Permanent URI for this collection https://rda.sliit.lk/handle/123456789/1253

Browse

Filters

20212

Advanced Search

Filter by

Settings

Author: search.filters.author.Jayasinghe, DSubject: search.filters.subject.Platform

Search Results

Now showing 1 - 2 of 2
  • Thumbnail Image
    PublicationEmbargo
    Traffic Monitoring Related Experimental Study for a Software-Defined Network Based Virtualized Security Functions Platform
    (IEEE, 2021-12-01) Gamage, T. C. T.; Rankothge, W. H.; Gamage, N. D. U.; Jayasinghe, D; Uwanpriya, S. D. L. S.; Amarasinghe, D. A.
    Cloud computing and virtualization technologies are rapidly evolving with new capabilities being added all the time. Security Functions Virtualization (SFV) is the latest addition to cloud services, where Virtualized Security Functions (VSFs) are offered as services by Cloud Service Providers (CSPs). CSPs are focusing more on implementing effective resource management approaches for the cloud infrastructure, considering specific requirements of VSFs. Network traffic monitoring is one of the most crucial aspects of cloud resource management, as monitoring helps CSPs to have a global view of the resource utilization and take necessary proactive management actions, specifically for VSFs.This experimental study focuses on exploring network traffic and resource monitoring for the traffic traverse via a cloud platform where VSFs are offered as a service. We have considered two approaches: periodic monitoring and continuous monitoring. The network traffic is monitored continuously, and resource utilization is monitored periodically. With the implemented monitored framework, CSPs are able to take proactive decisions on resource management, specially towards scale-out/in decisions and security management.
  • Thumbnail Image
    PublicationEmbargo
    Network Traffic Prediction for a Software Defined Network Based Virtualized Security Functions Platform
    (IEEE, 2021-12-06) Jayasinghe, D; Rankothge, W. H; Gamage, N. D. U; Gamage, T. C. T; Amarasinghe, D. A. H. M; Uwanpriya, S. D. L. S
    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%.