A model-based application autonomic manager with fine granular bandwidth control

In this paper, we propose and implement a machine learning based application autonomic management system that controls the bandwidth rates allocated to each scenario of a web application to postpone scaling out for as long as possible. Through experiments on Amazon AWS cloud, we demonstrate that the autonomic manager is able to quickly meet Service level Agreement (SLA) and reduce the SLA violations by 56% compared to a previous heuristic-based approach.