A Model-Based Approach Towards Real-Time Analytics in NFV Infrastructures

Abstract

Network Functions Virtualization (NFV) has recently gained much popularity in the research scene for the flexibility and programmability that it will bring with the software implementation of network functions on commercial off-the-shelf (COTS) hardware. To substantiate its roll out, a number of issues (e.g., COTS’ inherent performance and energy efficiency, virtualization overhead, etc.) must be addressed, in a scalable and sustainable manner. Numerous works in the scientific literature manifest the strong correlation of network key performance indicators (KPIs) with the burstiness of the traffic. This paper proposes a novel model-based analytics approach for profiling virtualized network function (VNF) workloads, towards real-time estimation of network KPIs (specifically, power and latency), based on an MX/G/1/SET queueing model that captures both the workload burstiness and system setup times (caused by interrupt coalescing and power management actions). Experimental results show good estimation accuracies for both VNF workload profiling and network KPI estimation, with respect to the input traffic and actual measurements, respectively. This demonstrates that the proposed approach can be a powerful tool for scalable and sustainable network/service management and orchestration.