Endowing the MIA Cloud Autoscaler with Adaptive Evolutionary and Particle Swarm Multi-Objective Optimization Algorithms

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

Ciudad de México

Conferencia; 20th Mexican International Conference on Artificial Intelligence, MICAI 2021; 2021

Sociedad Mexicana de Inteligencia Artficial

PSE (Parameter Sweep Experiments) applications represent a relevant class of computational applications in science, engineering and industry. These applications involve many computational tasks that are both resource-intensive and independent. For this reason, these applications are suited for Cloud environments. In this sense, Cloud autoscaling approaches are aimed to manage the execution of different kinds of applications on Cloud environments. One of the most recent approaches proposed for autoscaling PSE applications is MIA, which is based on the multi-objective evolutionary algorithm NSGA-III. We propose to endow MIA with other multi-objective optimization algorithms, to improve its performance. In this respect, we consider two well-known multi-objective optimization algorithms named SMS-EMOA and SMPSO, which have significant mechanic differences with NSGA-III. We evaluate MIA endowed with each of these algorithms, on three real-world PSE applications, considering resources available in Amazon EC2. The experimental results show that MIA endowed with each of these algorithms significantly outperforms MIA based on NSGA-III.