During the last few years, learning techniques have changed, both in basic education and in higher education. This change has been accompanied by new technologies such as Augmented Reality (AR) and Virtual Reality (AR). The combination of these technologies in education has allowed a greater immersion, positively affecting the learning and teaching processes. In addition, since the COVID-19 pandemic, this trend has been growing due to the diversity of the different fields of application of these technologies, such as heterogeneity in their combination and their different experiences. It is necessary to review the state of the art to determine the effectiveness of the application of these technologies in the field of university higher education. In the present paper, this aim is achieved by performing a systematic literature review from 2012 to 2022. A total of 129 papers were analyzed. Studies in our review concluded that the application of AR/VR improves learning immersion, especially in hospitality, medicine, and science studies. However, there are also negative effects of using these technologies, such as visual exhaustion and mental fatigue.

Business leaders may bemoan the burdens of governing IT, but the alternative could be much worse.

Resource management is a key factor in the performance and efficient utilization of cloud systems, and many research works have proposed efficient policies to optimize such systems. However, these policies have traditionally managed the resources individually, neglecting the complexity of cloud systems and the interrelation between their elements. To illustrate this situation, we present an approach focused on virtualized Hadoop for a simultaneous and coordinated management of virtual machines and file replicas. Specifically, we propose determining the virtual machine allocation, virtual machine template selection, and file replica placement with the objective of minimizing the power consumption, physical resource waste, and file unavailability. We implemented our solution using the non-dominated sorting genetic algorithm-II, which is a multi-objective optimization algorithm. Our approach obtained important benefits in terms of file unavailability and resource waste, with overall improvements of approximately 400% and 170% compared to three other optimization strategies. The benefits for the power consumption were smaller, with an improvement of approximately 1.9%.