Autonomous Intelligent VNF Profiling for Future Intelligent Network Orchestration - Nasim Ferdosian; Shadi Moazzeni; Pratchaya Jaisudthi; Yifei Ren; Himanshu Agrawal; Dimitra Simeonidou; Reza Nejabati (IEEE Transactions on Machine Learning in Communications and Networking)
In this article, we propose a profile-based data-driven analysis framework to extract and analyze the characteristics and behavior of virtualized network functions (VNFs) in virtualized networks from the resource and performance perspective. This framework represents some solutions for applying profiling information to analyze VNF-level service performance and discover resource and performance correlations. Different machine learning approaches deploy the resource and performance analysis outcomes to make some performance predictions for the proactive orchestration and management of the service life cycle. Although there have been a number of prior studies on VNF profiling, to the best of our knowledge, this article is the first study introducing an autonomous time-wise profiling method that provides insights on VNF behavior in both performance and resource utilization perspectives, in a real deployment environment. This helps to provide efficient resource allocation and performance plans to ensure service performance requirements, specified in the service level agreement, and prevent unnecessary life cycle management (LCM) actions such as VNF migration and scaling. We present a detailed evaluation to validate our method and a case study showing how the automated system improves the LCM decision by reducing the number of VNF migrations in a real-life scenario.