TY - JOUR AU - dos Santos, Allan Matheus Marques AU - Pinto, Raquel Coelho Gomes AU - Duarte, Julio Cesar AU - Schulze, Bruno Richard PY - 2022/12/28 Y2 - 2024/03/28 TI - Application of Profile Prediction for Proactive Scheduling JF - Revista de Informática Teórica e Aplicada JA - RITA VL - 29 IS - 3 SE - Regular Papers DO - 10.22456/2175-2745.120399 UR - https://seer.ufrgs.br/index.php/rita/article/view/120399 SP - 65-75 AB - <p>Today, cloud environments are widely used as execution platforms for most applications. In these environments, virtualized applications often share computing resources. Although this increases hardware utilization, resources competition can cause performance degradation, and knowing which applications can run on the same host without causing too much interference is key to a better scheduling and performance. Therefore, it is important to predict the resource consumption profile of applications in their subsequent iterations. This work evaluates the use of machine learning techniques to predict the increase or decrease in computational resources consumption. The prediction models are evaluated through experiments using real and benchmark applications. Finally, we conclude that some models offer significantly better performance when compared to the current trend of resource usage. These models averaged up to 94% on the F1 metric for this task.</p> ER -