Generating Resource and Performance Models for Service Function Chains: The Video Streaming Case

Understanding the behavior of the components of service function chains (SFCs) in different load situations is important for efficient and automatic management and orchestration of services. For this purpose and for practical research in network function virtualization in general, there is a great need for benchmarks and experimental data. In this paper, we describe our experiments for characterizing the relationship between resource demands of virtual network functions (VNFs) and the expected performance of the SFC, considering the individual performance of the VNFs as well as the interdependencies among VNFs within the SFC. We have designed our experiments focusing on video streaming, an important application in this context. We present examples of models for predicting the interdependence between resource demands and performance characteristics of SFCs using support vector regression and polynomial regression models. We also show practical evidence from our experiments that VNFs need to be benchmarked in their final chain setup, rather than individually, to capture important interdependencies that affect their performance. The data gathered from our experiments is publicly available.