A bio-inspired scheduler for minimizing makespan and flowtime of computational mechanics applications on federated clouds

Computational Mechanics (CM) concerns the use of computational methods to study phenomena under the principles of mechanics. A representative CM application is parameter sweep experiments (PSEs), which involves the execution of many CPU-intensive jobs and thus computing environments such as Clouds m...

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Detalles Bibliográficos
Publicado:	2016
Materias:	Ant colony optimization Cloud computing Computational mechanics Particle swarm optimization Scheduling Artificial intelligence Mechanics Ant Colony Optimization (ACO) Computing environments CPU-intensive Federated clouds Minimizing makespan Network latencies Simulated experiments Virtual machines Particle swarm optimization (PSO)
Acceso en línea:	https://bibliotecadigital.exactas.uba.ar/collection/paper/document/paper_10641246_v31_n3_p1731_Pacini http://hdl.handle.net/20.500.12110/paper_10641246_v31_n3_p1731_Pacini
Aporte de:	Biblioteca Digital - Facultad de Ciencias Exactas y Naturales (UBA) de Universidad de Buenos Aires

id	paper:paper_10641246_v31_n3_p1731_Pacini
record_format	dspace
spelling	paper:paper_10641246_v31_n3_p1731_Pacini2023-06-08T16:04:05Z A bio-inspired scheduler for minimizing makespan and flowtime of computational mechanics applications on federated clouds Ant colony optimization Cloud computing Computational mechanics Particle swarm optimization Scheduling Ant colony optimization Artificial intelligence Cloud computing Computational mechanics Mechanics Scheduling Ant Colony Optimization (ACO) Computing environments CPU-intensive Federated clouds Minimizing makespan Network latencies Simulated experiments Virtual machines Particle swarm optimization (PSO) Computational Mechanics (CM) concerns the use of computational methods to study phenomena under the principles of mechanics. A representative CM application is parameter sweep experiments (PSEs), which involves the execution of many CPU-intensive jobs and thus computing environments such as Clouds must be used. We focus on federated Clouds, where PSEs are processed via virtual machines (VM) that are lauched in hosts belonging to different datacenters, minimizing both the makespan and flowtime. Scheduling is performed at three levels: a) broker, where datacenters are selected based on their network latencies via three policies, b) infrastructure, where two bio-inspired schedulers based on Ant Colony Optimization (ACO) and Particle Swarm Optimization (PSO) for VM-host mapping in a datacenter are implemented, and c)VM, where jobs are assigned into the preallocated VMs based on job priorities. Simulated experiments performed with job data from two real PSEs show that our scheduling approach allows for a more agile job handling while reducing PSE makespan and flowtime. 2016 https://bibliotecadigital.exactas.uba.ar/collection/paper/document/paper_10641246_v31_n3_p1731_Pacini http://hdl.handle.net/20.500.12110/paper_10641246_v31_n3_p1731_Pacini
institution	Universidad de Buenos Aires
institution_str	I-28
repository_str	R-134
collection	Biblioteca Digital - Facultad de Ciencias Exactas y Naturales (UBA)
topic	Ant colony optimization Cloud computing Computational mechanics Particle swarm optimization Scheduling Ant colony optimization Artificial intelligence Cloud computing Computational mechanics Mechanics Scheduling Ant Colony Optimization (ACO) Computing environments CPU-intensive Federated clouds Minimizing makespan Network latencies Simulated experiments Virtual machines Particle swarm optimization (PSO)
spellingShingle	Ant colony optimization Cloud computing Computational mechanics Particle swarm optimization Scheduling Ant colony optimization Artificial intelligence Cloud computing Computational mechanics Mechanics Scheduling Ant Colony Optimization (ACO) Computing environments CPU-intensive Federated clouds Minimizing makespan Network latencies Simulated experiments Virtual machines Particle swarm optimization (PSO) A bio-inspired scheduler for minimizing makespan and flowtime of computational mechanics applications on federated clouds
topic_facet	Ant colony optimization Cloud computing Computational mechanics Particle swarm optimization Scheduling Ant colony optimization Artificial intelligence Cloud computing Computational mechanics Mechanics Scheduling Ant Colony Optimization (ACO) Computing environments CPU-intensive Federated clouds Minimizing makespan Network latencies Simulated experiments Virtual machines Particle swarm optimization (PSO)
description	Computational Mechanics (CM) concerns the use of computational methods to study phenomena under the principles of mechanics. A representative CM application is parameter sweep experiments (PSEs), which involves the execution of many CPU-intensive jobs and thus computing environments such as Clouds must be used. We focus on federated Clouds, where PSEs are processed via virtual machines (VM) that are lauched in hosts belonging to different datacenters, minimizing both the makespan and flowtime. Scheduling is performed at three levels: a) broker, where datacenters are selected based on their network latencies via three policies, b) infrastructure, where two bio-inspired schedulers based on Ant Colony Optimization (ACO) and Particle Swarm Optimization (PSO) for VM-host mapping in a datacenter are implemented, and c)VM, where jobs are assigned into the preallocated VMs based on job priorities. Simulated experiments performed with job data from two real PSEs show that our scheduling approach allows for a more agile job handling while reducing PSE makespan and flowtime.
title	A bio-inspired scheduler for minimizing makespan and flowtime of computational mechanics applications on federated clouds
title_short	A bio-inspired scheduler for minimizing makespan and flowtime of computational mechanics applications on federated clouds
title_full	A bio-inspired scheduler for minimizing makespan and flowtime of computational mechanics applications on federated clouds
title_fullStr	A bio-inspired scheduler for minimizing makespan and flowtime of computational mechanics applications on federated clouds
title_full_unstemmed	A bio-inspired scheduler for minimizing makespan and flowtime of computational mechanics applications on federated clouds
title_sort	bio-inspired scheduler for minimizing makespan and flowtime of computational mechanics applications on federated clouds
publishDate	2016
url	https://bibliotecadigital.exactas.uba.ar/collection/paper/document/paper_10641246_v31_n3_p1731_Pacini http://hdl.handle.net/20.500.12110/paper_10641246_v31_n3_p1731_Pacini
_version_	1768544423247872000

A bio-inspired scheduler for minimizing makespan and flowtime of computational mechanics applications on federated clouds

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