A Network Intelligence Architecture for Efficient VNF Lifecycle Management

Network softwarization paradigms such as SDN and NFV provide network operators with advantages in terms of scalability, cost and resource efficiency, as well as flexibility. However, in order to fully reap these benefits and cope with new challenges regarding the heterogeneity of user demands and an ever-growing service landscape, management and operation of such networks requires a high degree of automation that ensures fast and proactive decision making. With the recent success of machine learning (ML) across numerous domains, a shift from traditional rule-based policies towards ML-based approaches in the context of network management is taking place. Although many individual contributions cover use cases such as predicting various network characteristics or optimizing the configuration of components, a fully integrated architecture for achieving
Network Intelligence
is still missing. Hence, in this work, we propose such an architecture that combines the capabilities of softwarized networks with ML-based management. The contribution of this article is threefold: first, we present the proposed architecture alongside its components. Second, we implement a proof-of-concept version of all components in our OpenStack-based testbed. Finally, we demonstrate in a case study regarding VNF resource prediction how the proposed architecture can be used to generate realistic data sets to train and evaluate ML-based models for this task.