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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Guardado en:
Detalles Bibliográficos
Publicado: 2017
Materias:
Autoscaling
Cloud computing
Scheduling
Scientific workflows
Spot instances
Costs
Cloud infrastructures
Cost performance
Heuristic scheduling
Simulated experiments
State of the art
Cost reduction
Acceso en línea:https://bibliotecadigital.exactas.uba.ar/collection/paper/document/paper_02676192_v32_n4_p291_Monge
http://hdl.handle.net/20.500.12110/paper_02676192_v32_n4_p291_Monge
Aporte de:
Biblioteca Digital - Facultad de Ciencias Exactas y Naturales (UBA) de Universidad de Buenos Aires

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