Emulation as a Service (EaaS): A Plug-n-Play Framework for Benchmarking Network Analytics

Abstract

Real-time data generation and collection to analyse the network performance is difficult for large-scale networks having limited accessibility. In this paper we propose a framework which can provide realistic si/e-mulations, and generate synthetic data closer to real-time data that replaces the traditionally used deterministic and probabilistic models. This framework uses an emulation based platform to replicate real network scenarios. The emulator acts as a base layer with necessary APIs to enable customized inclusion of analytics services in a plug-and-play manner through the framework. This framework can be used to acquire data required for different Machine Learning (ML) models in order to reduce costly and time-consuming data collection effort in network analytics.