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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2017
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| Acceso en línea: | https://bibliotecadigital.exactas.uba.ar/collection/paper/document/paper_00457906_v_n_p_Monge http://hdl.handle.net/20.500.12110/paper_00457906_v_n_p_Monge |
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paper:paper_00457906_v_n_p_Monge2023-06-08T15:05:21Z Meta-heuristic based autoscaling of cloud-based parameter sweep experiments with unreliable virtual machines instances 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. 2017 https://bibliotecadigital.exactas.uba.ar/collection/paper/document/paper_00457906_v_n_p_Monge 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 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. |
| 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 |
| publishDate |
2017 |
| url |
https://bibliotecadigital.exactas.uba.ar/collection/paper/document/paper_00457906_v_n_p_Monge http://hdl.handle.net/20.500.12110/paper_00457906_v_n_p_Monge |
| _version_ |
1768544032098615296 |