Browsing by Author "Ariyawansa, M. M. T. R"

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    An Expermental Study on Load Balancing for a Software Defined Network based Virtualized Network Functions Platform
    (IEEE, 2021-12-06) Senevirathne, M. D. B. P; Rankothge, W. H; Gamage, N. D. U; Ariyawansa, M. M. T. R; Dewwiman, H. G. H; Suhail, A
    Network functionalities in conventional computer networks have been facilitated by implementing hardware middle-boxes. However, with the introduction of Virtualized Network Functions (VNFs) technologies, Cloud Service providers (CSPs) are able to offer VNFs as services to clients, along with general virtualized applications. CSPs provision and allocate resources to the VNFs, as required by the clients. An efficient cloud resource management approach plays an important role, in terms of continuing a successful CSP business model. To meet client Service Level Agreement (SLA) for Quality of Service (QoS), CSP is required to ensure that the virtualized entities are not overloaded with the processing, and workload is divided among virtualized entities adequately. Therefore, load balancing plays an important role, when offering VNFs as services to clients. This research focuses on exploring a load balancing algorithm for a cloud platform where VNFs are offered as a service. As the initial stage, we have used Weighted Round-Robin and Least Connection approaches and conducted an experimental study to compare the performances of the two approaches. Our results show that the weighted round robin algorithm performs better in terms of the workload distribution and average response time.
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    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. P
    With 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.