Browsing by Author "Jayasinghe, D."

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Embargo
Computer Vision for Autonomous Driving
(2021 3rd International Conference on Advancements in Computing (ICAC), SLIIT, 2021-12-09) Kanchana, B.; Peiris, R.; Perera, D.; Jayasinghe, D.; Kasthurirathna, D.
Computer vision in self-driving vehicles can lead to research and development of futuristic vehicles that can mitigate the road accidents and assist in a safer driving environment. By using the self-driving technology, the riders can be roamed to their destinations without using human interaction. But in recent times self-driving vehicle technology is still at the early stage. Mostly in the rushed areas like cities it becomes challenging to deploy such autonomous systems because even a small amount of data can cause a critical accident situation. In Order to increase the autonomous driving conditions computer vision and deep learning-based approaches are tended to be used. Finding the obstacles on the road and analyzing the current traffic flow are mainly focused areas using computer vision-based approaches. As well as many researchers using deep learning-based approaches like convolutional neural networks to enhance the autonomous driving conditions. This research paper focused on the evaluation of computer vision used in self-driving vehicles.
Embargo
Resource Utilization based Load Balancing for a Virtualized Security Functions Platform
(IEEE, 2021-12-01) Amarasinghe, D. A. H. M.; Rankothge, W. H.; Gamage, N. D. U.; Gamage, T. C. T.; Uwanpriya, S. D. L. S.; Jayasinghe, D.
Cloud Service Providers (CSPs) are moving to a Software Defined Networks (SDN) based approach to managing their network infrastructure. The introduction of Virtualized Security Functions (VSFs) and offering them as a service has to make SDN more popular among CSPs because SDN brings advantages such as flexibility, elasticity, and easy management. When offering VSFs, resource management is one of the important aspects, especially considering the dynamic nature of the traffic, where traffic increases and decreases dynamically over time. In parallel to the traffic changes, resources also have to increase/ decrease and traffic load must be balanced over the resources. This research focuses on exploring a resource utilization-based load balancing algorithm, that considers memory and CPU utilization of the VSFs. We have used Mininet simulation to build an SDN-based cloud architecture with VSFs, and POX controller to explore the performances of the proposed algorithm, in terms of average workload and average response time. Our results show that the resource utilization-based load balancing algorithm balances the workload and traffic adequately, within an acceptable time.