An Intelligent Traffic Entropy Learning-Based Load Management Model for Cloud Networks

This letter proposes a novel traffic entropy learning based load prediction and management model that envisages improvement of load distribution by minimization of performance degradation due to traffic prediction errors. The entropy determines the variance considering dynamic surge and plunge of the traffic periodically and suggests to acquire sufficient number of active physical machines (PMs) to render efficacious services. The experimental simulation and comparison of the proposed model with existing approaches reveal that it significantly improves resource utilization up to 21.5% with reduction of active servers and energy consumption up to 26.5% and 11.7%, respectively.