ENVI: Elastic resource flexing for Network function Virtualization

Dynamic and elastic resource allocation to Virtual Network Functions (VNFs) in accordance with varying workloads is a must for realizing promised reductions in capital and operational expenses in Network Functions Virtualization (NFV). However, workload heterogeneity and complex relationships between resources allocated to a VNF and the resulting capacity makes elastic resource flexing a challenging task. We propose an NFV resource flexing system, ENVI, that uses a combination of VNF-level features and infrastructure-level features to construct a machine-learning-based decision engine for detecting resource flexing events. ENVI also extracts the dependence relationship among VNFs in deployed Service Function Chains (SFCs) to carefully plan the sequence of resource flexing steps upon scaling detection. We present preliminary results for the accuracy of ENVI’s resource flexing decision engine with two different VNFs, namely, the caching proxy Squid and the intrusion detection system Suricata. Our preliminary results show that using a combination of features to train a neural network model is a promising approach for scaling detection.