Load Forecasting Mechanism for e-Learning Infrastructures Using Exponential Smoothing

Thanks to the development of cloud technologies, the way how computing is understood has evolved from "computer guided" to "user guided" systems. That is, initially, computers had static software features in which users sharing them had to "fit", but now there is a shift to dynamic systems in which it is the computer which has to fit into the users\´ needs. This allows more efficient use of computing resources, improving the revenue and enhancing the Quality of Service (QoS) received by users. In order to 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. This reduces the power consumption and enhances the revenue of the system. This paper presents an algorithm to perform resource provisioning on the machines of the technological infrastructure of our University, so that they can be efficiently managed. This algorithm is based on load forecasts created using Exponential Smoothing.