Browsing by Author "Suhail, S. A. A"

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    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. P
    Software 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.
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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.