A model for quantifying performance degradation in virtual network function service chains

Virtual Network Functions (VNFs) can be chained and provisioned on demand, providing elasticity and dynamicity to the network. Due to the interdependencies between VNFs, resulting service chains may not work as expected, and because of that, it is crucial to determine which VNFs are having a negative impact on the service quality. In this paper, we introduce a model to quantify the guiltiness of a VNF on being a bottleneck in a service chain, which provides a metric that estimates the impact on processing delay. In addition, we propose an adaptive algorithm, based on linear regression and neural networks, to adjust the model parameters according to the environment particularities, such as the type and number of VNFs. We show through an experimental evaluation that the guiltiness metric faithfully characterizes end-service performance, by identifying up to 94% of the bottleneck VNFs in the analyzed scenarios. Also, we provide artifacts for researchers to reproduce our results in other scenarios.