Application of Profile Prediction for Proactive Scheduling

Allan Matheus Marques dos Santos; Raquel Coelho Gomes  Pinto; Julio Cesar  Duarte; Bruno Richard  Schulze

doi:10.22456/2175-2745.120399

Authors

Allan Matheus Marques dos Santos Instituto Militar de Engenharia
Raquel Coelho Gomes Pinto Instituto Militar de Engenharia https://orcid.org/0000-0003-0593-8105
Julio Cesar Duarte Instituto Militar de Engenharia https://orcid.org/0000-0001-6656-1247
Bruno Richard Schulze National Laboratory of Scientific Computing

DOI:

https://doi.org/10.22456/2175-2745.120399

Keywords:

application profile, scheduling, cloud computing, intelligent agents

Abstract

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.

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