A Deep Learning Approach to VNF Resource Prediction using Correlation between VNFs

Abstract

Software-Defined Networking (SDN) and Network Function Virtualization (NFV) greatly facilitate network service management. Specifically, these new network paradigms help manage the network environment dynamically and cost-efficiently. Virtual Network Function (VNF) and Service Function Chaining (SFC) are important aspects of the NFV environment. In terms of NFV management, resource demand of VNFs can be predicted at a future time to handle Quality of Service (QoS) and resource allocation problems efficiently. Hence, researchers study and build a management system where machine-learning-based predictions of VNF information are used to handle auto-scaling, deployment and migration of VNFs. In addition, in recent studies, these systems have involved SFC to obtain useful information, not just a lone VNF. However, not many of studies explain clearly how chaining dependency among VNFs in a SFC can be used to predict future resource demand of a VNF. In this paper, we introduce VNF resource prediction machine learning model that maximizes the benefits of using SFC. Then, we compare several machine learning models and analyze how SFC data can help predict resource usage patterns of VNFs. We also show benefits of Attention model to improve prediction accuracy and convergence time through experiments.