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Reinforcement Learning (RL) has demonstrated a great potential for automatically solving decision-making problems in complex uncertain environments. RL proposes a computational approach that allows learning through interaction in an environment with stochastic behavior, where agents take actions to maximize some cumulative short-term and long-term rewards. Some of the most impressive results have been shown in Game Theory where agents exhibited superhuman performance in games like Go or Starcraft 2, which led to its gradual adoption in many other domains, including Cloud Computing. Therefore, RL appears as a promising approach for Autoscaling in Cloud since it is possible to learn transparent (with no human intervention), dynamic (no static plans), and adaptable (constantly updated) resource management policies to execute applications. These are three important distinctive aspects to consider in comparison with other widely used autoscaling policies that are defined in an ad-hoc way or statically computed as in solutions based on meta-heuristics. Autoscaling exploits the Cloud elasticity to optimize the execution of applications according to given optimization criteria, which demands to decide when and how to scale-up/down computational resources, and how to assign them to the upcoming processing workload. Such actions have to be taken considering that the Cloud is a dynamic and uncertain environment. Motivated by this, many works apply RL to the autoscaling problem in the Cloud. In this work, we survey exhaustively those proposals from major venues, and uniformly compare them based on a set of proposed taxonomies. We also discuss open problems and prospective research in the area., 40 pages, 9 figures

This paper concerns the provision of new architectural forms in reinforced concrete, obtaining volumes with a double curve surfaces at a reasonable cost, constructed using flexible formwork. Through design research (research by design) 8 types of columns made of in situ reinforced concrete are achieved. An architectonic element with optimal spatial quality and an organic form, with a surface not requiring another finish is the end in mind. The construction process is relatively simple given the results obtained and the construction of prototypes during building work on site opens up the possibility of using this technology on building sites everywhere reinforced concrete is available. Resumen: Este artículo trata de la construcción de nuevas formas en hormigón armado para la arquitectura, al obtener volúmenes con superficies de doble curvatura a un costo razonable, construidos con moldajes flexibles. Se han obtenido por medio de la investigación por diseño 8 tipos de columnas de hormigón armado realizadas in situ, en la obra. Es un elemento arquitectónico de gran calidad espacial con una forma orgánica, cuya superficie de óptima terminación no requiere otro acabado. El proceso constructivo es relativamente simple para el resultado obtenido y su construcción en el sitio de la obra abre la posibilidad de erigirlo en cualquier parte donde se pueda emplear el hormigón armado.