2022
DOI: 10.3390/app12094576
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Performance Analysis of Selected Machine Learning Techniques for Estimating Resource Requirements of Virtual Network Functions (VNFs) in Software Defined Networks

Abstract: Rapid development in the field of computer networking is now demanding the application of Machine Learning (ML) techniques in the traditional settings to improve the efficiency and bring automation to these networks. The application of ML to existing networks brings a lot of challenges and use-cases. In this context, we investigate different ML techniques to estimate resource requirements of complex network entities such as Virtual Network Functions (VNFs) deployed in Software Defined Networks (SDN) environmen… Show more

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Cited by 2 publications
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“…In the study conducted by Faheem et al [14], the authors explored a spectrum of machine learning techniques dedicated to estimating the resource requirements of intricate network entities, particularly Virtual Network Functions (VNFs) within a software-defined networking environment. Their focus primarily centered on deciphering the resource demands of VNFs, notably the central processing unit (CPU) consumption during the processing of input traffic.…”
Section: Related Workmentioning
confidence: 99%
“…In the study conducted by Faheem et al [14], the authors explored a spectrum of machine learning techniques dedicated to estimating the resource requirements of intricate network entities, particularly Virtual Network Functions (VNFs) within a software-defined networking environment. Their focus primarily centered on deciphering the resource demands of VNFs, notably the central processing unit (CPU) consumption during the processing of input traffic.…”
Section: Related Workmentioning
confidence: 99%