Network Function Modeling and Performance Estimation

Abstract

This work introduces a methodology for the modelization of network functions focused on the identification of recurring execution patterns as basic building blocks and aimed at providing a platform independent representation. By mapping each modeling building block on specific hardware, the performance of the network function can be estimated in termsof maximum throughput that the network function can achieve on the specific execution platform. The approach is such that once the basic modeling building blocks have been mapped, the estimate can be computed automatically for any modeled network function. Experimental results on several sample network functions show that although our approach cannot be very accurate without taking in consideration traffic characteristics, it is very valuable for those application where even loose estimates are key. One such example is orchestration in network functions virtualization (NFV) platforms, as well as in general virtualization platforms where virtual machine placement is based also on the performance of network services offered to them. Being able to automatically estimate the performance of a virtualized network function (VNF) on different execution hardware, enables optimal placement of VNFs themselves as well as the virtual hosts they serve, while efficiently utilizing available resources.