An NSGA-III-based Multi-objective Intelligent Autoscaler for Executing Engineering Applications in Cloud Infrastructures [Qualis B2]

ELINA PACINI; GUILLERMO RODRÍGUEZ; DAVID MONGE; VIRGINIA YANNNIBELLI; CRISTIAN MATEOS

Ciudad de México

Conferencia; 19th Mexican International Conference on Artificial Intelligence; 2020

Universidad Panamericana

Parameter Sweep Experiments (PSEs) are commonplace to performcomputer modelling and simulation at large in the context of industrial, engineeringand scientific applications. PSEs require numerous computational resourcessince they involve the execution of many CPU-intensive tasks. Distributed computingenvironments such as Clouds might help to fulfill these demands, andconsequently the need of Cloud autoscaling strategies for the efficient managementof PSEs arise. The Multi-objective Intelligent Autoscaler (MIA) is proposedto address this problem, which is based on the Non-dominated Sorting GeneticAlgorithm III (NSGA-III), while aiming to minimize makespan and cost. MIAis assessed utilizing the CloudSim simulator with three study cases coming fromreal-world PSEs and current characteristics of Amazon EC2. Experiments showthat MIA significantly outperforms the only PSE autoscaler (MOEA autoscaler)previously reported in the literature, to solve different instances of the problem.