Enhancing Service Management Systems with Machine Learning in Fog-to-Cloud Networks

With the fog-to-cloud hybrid computing systems emerging as a promising networking architecture, particularly interesting for IoT scenarios, there is an increasing interest in exploring and developing new technologies and solutions to achieve high performances of these systems. One of these solutions includes machine learning algorithms implementation. Even without defined and standardized way of using machine learning in fog-to-cloud systems, it is obvious that machine learning capabilities of autonomous decision making would enrich both fog computing and cloud computing network nodes. In this paper, we propose a service management system specially designed to work in fog-to-cloud architectures, followed with a proposal on how to implement it with different machine learning solutions. We first show the global overview of service management system functionality with the current specific design for each of its integral components and, finally, we show the first results obtained with machine learning algorithm for its component in charge of traffic prediction.