A Comparative Analysis of NSGA-II and NSGA-III for Autoscaling Parameter Sweep Experiments in the Cloud [JCR]

VIRGINIA YANNIBELLI; ELINA PACINI; DAVID MONGE; CRISTIAN MATEOS; GUILLERMO RODRIGUEZ

SCIENTIFIC PROGRAMMING

IOS PRESS

Lugar: Amsterdam; Año: 2020

1058-9244

The Cloud Computing paradigm is focused on the provisioning of reliable and scalable virtual infrastructures that deliver execution and storage services. This paradigm is particularly suitable to solve resource-greedy scientific computing applications such as Parameter Sweep Experiments (PSE). Through the implementation of autoscalers, the virtual infrastructure can be scaled up and down by acquiring or terminating instances of virtual machines (VM) at the time that application tasks are being scheduled. In this paper, we extend an existing study centered in a state-of-the-art autoscaler called Multi-objective Evolutionary Autoscaler (MOEA). MOEA uses a multi-objective optimization algorithm to determine the set of possible virtual infrastructure settings. In this context, the performance of MOEA is greatly influenced by the underlying optimization algorithm used and its tuning. Therefore, we analyze two well-known multi-objective evolutionary algorithms(NSGA-II and NSGA-III) and how they impact on the performance of the MOEA autoscaler. Simulated experiments with three real-world PSE sshow that MOEA gets significantly improved when using NSGA-III instead of NSGA-II due to the former provides a better exploitation versus exploration trade-off.