Model-based analytics for profiling workloads in virtual network functions

Abstract

With the flexibility and programmability levels offered by Network Functions Virtualization (NFV), it is expected to catalyze the upcoming “softwarization” of the network through software implementation of networking functionalities on virtual machines (VMs). While looking into the different issues thrown at NFV, numerous works have demonstrated how performance, power consumption and, consequently, the optimal resource configuration and VM allocation vary with the statistical features of the workload — specifically, the “burstiness” of the traffic. This paper proposes a model-based analytics approach for profiling (virtual) network function (VNF) workloads that captures traffic burstiness, considering — and adding value to — hardware/software performance monitor counters (PMCs) available in Linux host servers. Results show good estimation accuracies for the chosen PMCs, which can be useful to enhance current methods for finegrained provisioning, usage-based pricing and anomaly detection, and facilitate the way towards an agile network.