Analysis of ML Algorithms to Support Elastic Service Chaining in eHealth Vertical Applications

The efficient design of SFC-enabled eHealth applications requires an accurate provision of the underlying infrastructure. This provision requires both computing and networking resources to meet stringent QoS requirements under any conditions of service demand. Cloud providers often offer automatic elasticity strategies based on monitoring specific metrics that lead to a waste of resources, time/energy consumption, and the problem of starvation with competing services. Our findings provide evidence that proactive-based elasticity overcomes these issues, when assisted by Machine Learning (ML) methods for predicting Internet traffic load. An optimal autoscaling algorithm depends on high precision and fast predictions to provide accurate results. Thus, this paper assesses ML algorithms to support SFC-enabled eHealth vertical applications. The experimental results suggest that the evaluated models achieved similar accuracy metrics, with an MLP architecture delivering the best performance in terms of time training and average prediction time.