KSN: Modeling and simulation of knowledge using machine learning in NFV/SDN-based networks

Abstract

The rapid advancement of networking and computing technologies has led to the emergence of a wide range of diverse dynamic network services that present networks with new challenges, including in the provision and management of services. Recently, SDN and NFV technologies can be effective solutions in the dynamic management of network functions. For dealing with dynamic and complex problems in the field of NFV and SDN issues, the use of knowledge-based systems can be a good solution that has received more and more attention from researchers and developers. Despite the increasing attention to the use of knowledge such as machine learning and deep learning methods in the NFV and SDN field, the lack of a simulation framework with the ability to knowledge concept to implement and evaluate new methods has created a gap. In order to evaluate a new method on real platforms, a large-scale infrastructure has to be used, which faces challenges such as availability, cost, time and implementation difficulties. Since the existing simulation frameworks for networks using NFV and SDN technologies (e.g. CloudsimSDNNFV) do not support the knowledge-based concept, to fill this gap, we propose a highly extensible, interoperable, and scalable simulation framework for developing knowledge-based methods such as machine learning algorithms in the field of NFV and SDN, such as virtualized network functions auto-scaling, placement and migration. Also, for the proposed Knowledge-defined SDN/NFV (KSN) framework, an architecture is provided by defining the knowledge plane in the SDN/NFV integration structure. In addition, a new auto-scaling method based on the prediction of NFV resources consumption using machine learning has been proposed and simulated to demonstrate the capabilities of the framework and its evaluation. As we know, this is the first machine learning-based method in the NFV field, including VNF auto-scaling, which has been simulated in a network environment, and the impact of using machine learning has been practically evaluated and observed. The evaluation results show that the proposed method has a better impact on the SLA violation rate and resource usage than ElasticSFC and the basic version (without any auto-scaling method) and therefore it can be concluded that resource prediction is an effective and efficient solution for earlier decision making and VNF auto-scaling.