INVESTIGADORES
MATEOS DIAZ Cristian Maximiliano
artículos
Título:
Meta-heuristic based Autoscaling of Cloud-based Parameter Sweep Experiments with Unreliable Virtual Machines Instances [JCR]
Autor/es:
DAVID MONGE; ELINA PACINI; CRISTIAN MATEOS; CARLOS GARCÍA GARINO
Revista:
COMPUTERS & ELECTRICAL ENGINEERING
Editorial:
PERGAMON-ELSEVIER SCIENCE LTD
Referencias:
Lugar: Amsterdam; Año: 2017
ISSN:
0045-7906
Resumen:
Cloud Computing is the delivery of on-demand computing resources over the Internet on a pay-per-use basis and is very useful to execute scientific experiments such as parameter sweep experiments (PSEs). When PSEs are executed it is important to reduce both the makespan and monetary cost. We propose a novel tri-objective formulation for the PSEs autoscaling problem considering unreliable virtual machines (VM) pursuing the minimization of makespan, monetary cost and probability of failures. We also propose a new autoscaler based on the Non-dominated Sorting Genetic Algorithm II able to automatically determine the right amount for each type of VM and pricing scheme, as well as the bid prices for the spot instances. Experiments show that the proposed autoscaler achieves great improvements in terms of makespan and cost when it is compared against Scaling First and Spot Instances Aware Autoscaling.
