Cloud-based e-learning infrastructures with load forecasting mechanism based on Exponential Smoothing: A use case

The development of cloud technologies allows the implementation of scalable, versatile, and customized systems, constructed on-demand. This allows more efficient use of computing resources, improving the revenue of the system and enhancing the Quality of Service (QoS) received by users while minimizing the power consumption of the machines. Several research works conclude that in order to efficiently manage a cloud-based infrastructure (meaning, deploy computing resources when needed without affecting negatively to the QoS perceived by users), accurate predictions on the load of machines should be made. Thanks to this, resources can be ready to use when users need them, and shutdown when they are not needed - thus reducing the power consumption and enhancing the revenue of the system. This paper presents algorithms to perform forecasts of the load of machines based on Exponential Smoothing (ES), so that the machines of the technological infrastructure of our University can be efficiently managed. Furthermore, algorithms to perform monitoring and provision of resources based on load forecasts are presented. The usefulness of these algorithms is illustrated by means of a use case based on the e-learning facilities of our University. This use case shows that thanks to the use of cloud technologies, enhanced with the developed algorithms for load forecasting and provision of resources, better use of resources and lower power consumption can be achieved, without affecting the QoS received by user.