Research Publications Authored by SLIIT Staff
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This collection includes all SLIIT staff publications presented at external conferences and published in external journals. The materials are organized by faculty to facilitate easy retrieval.
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Publication Embargo 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.Publication Embargo NetEye: Network Monitoring for a Software Defined Network based Virtualized Network Functions Platform(IEEE, 2021-12-01) Rankothge, W. H; Gamage, N. D. U; Ariyawansa, M. M. T. R; Suhail, S. A. A; Dewwiman, H. G. H.; Senevirathne, M. D. B. PWith the introduction of Virtualized Network Functions Virtualization (VNFs), Cloud Service Providers (CSPs) allocate resources and deploy network functions as virtualized entities in the cloud. With the dynamic changes in the traffic and workload, initially allocated resources have to be increased or decreased to maintain the Service Level Agreement (SLA). Therefore, CSPs rely on network monitoring approaches to maintain an effective and efficient resource management process. However, the monitoring process itself creates an overhead to the performance of the network. Monitoring algorithms consume the CPU and memory resources of the cloud infrastructure during their execution. Therefore, selecting an appropriate monitoring approach is important, especially in a resource-constrained network. In this research, we have explored two monitoring approaches: continuous and periodic, and compared their performances in terms of memory and CPU utilization.Publication Embargo A Deep Learning Model Optimized with Genetic Algorithms for Resource Allocation of Virtualized Network Functions(IEEE, 2021-12-01) Rankothge, W. H; Gamage, N. D. U; Suhail, S. A. A; Ariyawansa, M. M. T. R.; Dewwiman, H. G. H; Senevirathne, M. D. B. PSoftware Defined Networking (SDN) has gained a significant attention of Cloud Service providers (CSPs) for managing their network infrastructure. With the popularity of services such as virtualized applications and Virtualized Network Functions (VNFs), many organizations are outsourcing their entire data centers to the CSPs. From the perspective of CSPs, effective and efficient cloud resource management plays an important role, in terms of continuing a successful business model. This research focuses on proposing a resource allocation algorithm for a cloud platform where VNFs are offered as a service. It is a tier-based resource allocation approach, where different resource tiers are defined in terms of network bandwidth, processor speed, RAM, vCPUs and number of users. Once the client's request is submitted for VNFs, we have used a deep learning approach (a Keras model which was optimized using Genetic Algorithms) to forecast the most suitable resource tier. Our results show that the proposed resource allocation algorithms can forecasts the most suitable resource tier for given scenario, in the order of seconds, with high accuracy.Publication Embargo 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. SSoftware-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%.