Provisioning Computational Resources for Cloud-Based e-Learning Platforms Using Deep Learning Techniques

The use of e-learning technologies is growing even faster due to the existing conditions where virtual setups temporarily replace traditional classroom environments. Service infrastructure support for e-learning has moved to the cloud. For this reason, the efficient provisioning of resources for such platforms, which is achieved through prediction, is very relevant. The existing techniques for predicting the use of resources in the cloud are not designed with e-learning's specific requirements. This paper presents a neural network-based model for predicting the usage of computational resources for e-learning platforms. This model consists of a series of interconnected neural networks used to predict values for variables of interest, such as Random Access Memory (RAM) usage and Central Processing Unit (CPU) usage. Using data collected from a high school real scenario, we analyzed and used it to train and validate our neural network-based model. This scenario consisted of a Moodle server deployed in a Google Virtual Machine with a configured course and its contents. Each student performed a series of activities while connected to it. Our proposed model achieves high accuracy. The obtained results are promising, paving the way towards constructing software tools for provisioning computational resources on demand for e-learning platforms.