Autoscaling scientific workflows on the cloud by combining on-demand and spot instances
Autoscaling strategies achieve efficient and cheap executions of scientific workflows running in the cloud by determining appropriate type and amount of virtual machine instances to use while scheduling the tasks/data. Current strategies only consider on-demand instances ignoring the advantages of a...
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| Acceso en línea: | http://hdl.handle.net/20.500.12110/paper_02676192_v32_n4_p291_Monge |
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todo:paper_02676192_v32_n4_p291_Monge2023-10-03T15:13:29Z Autoscaling scientific workflows on the cloud by combining on-demand and spot instances Monge, D.A. Gari, Y. Mateos, C. Garino, C.G. Autoscaling Cloud computing Scheduling Scientific workflows Spot instances Cloud computing Costs Scheduling Autoscaling Cloud infrastructures Cost performance Heuristic scheduling Scientific workflows Simulated experiments Spot instances State of the art Cost reduction Autoscaling strategies achieve efficient and cheap executions of scientific workflows running in the cloud by determining appropriate type and amount of virtual machine instances to use while scheduling the tasks/data. Current strategies only consider on-demand instances ignoring the advantages of a mixed cloud infrastructure comprising also spot instances. Although the latter type of instances are subject to failures and therefore provide an unreliable infrastructure, they potentially offer significant cost and time improvements if used wisely. This paper discusses a novel autoscaling strategy with two features. First, it combines both types of instances to acquire a better cost-performance balance in the infrastructure. And second, it uses heuristic scheduling to deal with the unreliability of spot instances. Simulated experiments based on 4 scientific workflows showed substantial makespan and cost reductions of our strategy when compared with a reference strategy from the state of the art entitled Scaling First These promising results represent a step towards new and better strategies for workflow autoscaling in the cloud. © 2017 CRL Publishing Ltd. JOUR info:eu-repo/semantics/openAccess http://creativecommons.org/licenses/by/2.5/ar http://hdl.handle.net/20.500.12110/paper_02676192_v32_n4_p291_Monge |
| institution |
Universidad de Buenos Aires |
| institution_str |
I-28 |
| repository_str |
R-134 |
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Biblioteca Digital - Facultad de Ciencias Exactas y Naturales (UBA) |
| topic |
Autoscaling Cloud computing Scheduling Scientific workflows Spot instances Cloud computing Costs Scheduling Autoscaling Cloud infrastructures Cost performance Heuristic scheduling Scientific workflows Simulated experiments Spot instances State of the art Cost reduction |
| spellingShingle |
Autoscaling Cloud computing Scheduling Scientific workflows Spot instances Cloud computing Costs Scheduling Autoscaling Cloud infrastructures Cost performance Heuristic scheduling Scientific workflows Simulated experiments Spot instances State of the art Cost reduction Monge, D.A. Gari, Y. Mateos, C. Garino, C.G. Autoscaling scientific workflows on the cloud by combining on-demand and spot instances |
| topic_facet |
Autoscaling Cloud computing Scheduling Scientific workflows Spot instances Cloud computing Costs Scheduling Autoscaling Cloud infrastructures Cost performance Heuristic scheduling Scientific workflows Simulated experiments Spot instances State of the art Cost reduction |
| description |
Autoscaling strategies achieve efficient and cheap executions of scientific workflows running in the cloud by determining appropriate type and amount of virtual machine instances to use while scheduling the tasks/data. Current strategies only consider on-demand instances ignoring the advantages of a mixed cloud infrastructure comprising also spot instances. Although the latter type of instances are subject to failures and therefore provide an unreliable infrastructure, they potentially offer significant cost and time improvements if used wisely. This paper discusses a novel autoscaling strategy with two features. First, it combines both types of instances to acquire a better cost-performance balance in the infrastructure. And second, it uses heuristic scheduling to deal with the unreliability of spot instances. Simulated experiments based on 4 scientific workflows showed substantial makespan and cost reductions of our strategy when compared with a reference strategy from the state of the art entitled Scaling First These promising results represent a step towards new and better strategies for workflow autoscaling in the cloud. © 2017 CRL Publishing Ltd. |
| format |
JOUR |
| author |
Monge, D.A. Gari, Y. Mateos, C. Garino, C.G. |
| author_facet |
Monge, D.A. Gari, Y. Mateos, C. Garino, C.G. |
| author_sort |
Monge, D.A. |
| title |
Autoscaling scientific workflows on the cloud by combining on-demand and spot instances |
| title_short |
Autoscaling scientific workflows on the cloud by combining on-demand and spot instances |
| title_full |
Autoscaling scientific workflows on the cloud by combining on-demand and spot instances |
| title_fullStr |
Autoscaling scientific workflows on the cloud by combining on-demand and spot instances |
| title_full_unstemmed |
Autoscaling scientific workflows on the cloud by combining on-demand and spot instances |
| title_sort |
autoscaling scientific workflows on the cloud by combining on-demand and spot instances |
| url |
http://hdl.handle.net/20.500.12110/paper_02676192_v32_n4_p291_Monge |
| work_keys_str_mv |
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1807322163918667776 |