Predicting Throughput of Cloud Network Infrastructure Using Neural Networks

Abstract

Throughput prediction of network infrastructures is an important aspect of capacity planning, scheduling, resource management, route selection and other network functions. In this paper, we describe throughput measurements collected over a network infrastructure that supports cloud computing spanning the globe. We train deep learning models to predict TCP throughput using these measurements, which show performance improvements with buffer tuning and parallel streams. We also compare the accuracy of machine learning and conventional methods in predicting both single thread and mutli-stream throughput in a public cloud environment.