Towards Accurate and Scalable Performance Prediction for Automated Service Design in NFV

Abstract

Automatizing the process of designing communication services in network function virtualization (NFV) is important because it may reduce provisioning time and lead to more efficient designs. The design process involves solving performance constraints imposed by service level agreements (SLAs), which in turn requires accurate and fast performance prediction. However, effects such as resource contention make performance prediction in virtualized environments challenging when large numbers of possible combinations of software and hardware are considered. The key to scalability lies in finding a componentized approach that reduces the number of model degrees of freedom while still allowing high accuracy. In this work, we propose a componentized approach based on feed-forward networks that are composited from software and hardware models. Model parameter data is obtained from a machine learning technique which is fed using data generated from automatized offline performance measurements. An evaluation showed that our technology achieves a prediction accuracy close to 95% and prediction evaluation times of a few milliseconds.