Meta-heuristic based autoscaling of cloud-based parameter sweep experiments with unreliable virtual machines instances

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...

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Autores principales: Monge, D.A., Pacini, E., Mateos, C., García Garino, C.
Formato: INPR
Materias:
Autoscaling
Evolutive algorithms
Multi-objective optimization
Parameter sweeping
Cloud computing
Costs
Economics
Genetic algorithms
Multiobjective optimization
Network security
Optimization
Virtual machine
Monetary costs
Non-dominated sorting genetic algorithm - ii
On-demand computing
Probability of failure
Scientific experiments
Parameter estimation
Acceso en línea:http://hdl.handle.net/20.500.12110/paper_00457906_v_n_p_Monge
Aporte de:
Biblioteca Digital - Facultad de Ciencias Exactas y Naturales (UBA) de Universidad de Buenos Aires
id todo:paper_00457906_v_n_p_Monge
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spelling todo:paper_00457906_v_n_p_Monge2023-10-03T14:52:04Z Meta-heuristic based autoscaling of cloud-based parameter sweep experiments with unreliable virtual machines instances Monge, D.A. Pacini, E. Mateos, C. García Garino, C. Autoscaling Evolutive algorithms Multi-objective optimization Parameter sweeping Cloud computing Costs Economics Genetic algorithms Multiobjective optimization Network security Optimization Virtual machine Autoscaling Evolutive algorithms Monetary costs Non-dominated sorting genetic algorithm - ii On-demand computing Parameter sweeping Probability of failure Scientific experiments Parameter estimation 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. © 2017 Elsevier Ltd. INPR info:eu-repo/semantics/openAccess http://creativecommons.org/licenses/by/2.5/ar http://hdl.handle.net/20.500.12110/paper_00457906_v_n_p_Monge
institution Universidad de Buenos Aires
institution_str I-28
repository_str R-134
collection Biblioteca Digital - Facultad de Ciencias Exactas y Naturales (UBA)
topic Autoscaling
Evolutive algorithms
Multi-objective optimization
Parameter sweeping
Cloud computing
Costs
Economics
Genetic algorithms
Multiobjective optimization
Network security
Optimization
Virtual machine
Autoscaling
Evolutive algorithms
Monetary costs
Non-dominated sorting genetic algorithm - ii
On-demand computing
Parameter sweeping
Probability of failure
Scientific experiments
Parameter estimation
spellingShingle Autoscaling
Evolutive algorithms
Multi-objective optimization
Parameter sweeping
Cloud computing
Costs
Economics
Genetic algorithms
Multiobjective optimization
Network security
Optimization
Virtual machine
Autoscaling
Evolutive algorithms
Monetary costs
Non-dominated sorting genetic algorithm - ii
On-demand computing
Parameter sweeping
Probability of failure
Scientific experiments
Parameter estimation
Monge, D.A.
Pacini, E.
Mateos, C.
García Garino, C.
Meta-heuristic based autoscaling of cloud-based parameter sweep experiments with unreliable virtual machines instances
topic_facet Autoscaling
Evolutive algorithms
Multi-objective optimization
Parameter sweeping
Cloud computing
Costs
Economics
Genetic algorithms
Multiobjective optimization
Network security
Optimization
Virtual machine
Autoscaling
Evolutive algorithms
Monetary costs
Non-dominated sorting genetic algorithm - ii
On-demand computing
Parameter sweeping
Probability of failure
Scientific experiments
Parameter estimation
description 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. © 2017 Elsevier Ltd.
format INPR
author Monge, D.A.
Pacini, E.
Mateos, C.
García Garino, C.
author_facet Monge, D.A.
Pacini, E.
Mateos, C.
García Garino, C.
author_sort Monge, D.A.
title Meta-heuristic based autoscaling of cloud-based parameter sweep experiments with unreliable virtual machines instances
title_short Meta-heuristic based autoscaling of cloud-based parameter sweep experiments with unreliable virtual machines instances
title_full Meta-heuristic based autoscaling of cloud-based parameter sweep experiments with unreliable virtual machines instances
title_fullStr Meta-heuristic based autoscaling of cloud-based parameter sweep experiments with unreliable virtual machines instances
title_full_unstemmed Meta-heuristic based autoscaling of cloud-based parameter sweep experiments with unreliable virtual machines instances
title_sort meta-heuristic based autoscaling of cloud-based parameter sweep experiments with unreliable virtual machines instances
url http://hdl.handle.net/20.500.12110/paper_00457906_v_n_p_Monge
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AT mateosc metaheuristicbasedautoscalingofcloudbasedparametersweepexperimentswithunreliablevirtualmachinesinstances
AT garciagarinoc metaheuristicbasedautoscalingofcloudbasedparametersweepexperimentswithunreliablevirtualmachinesinstances
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