A Three-level Scheduler to Execute Scientific Experiments on Federated Clouds

For executing current simulated scientific experiments it is necessary to have huge amounts of computing power. A solution path to this problem is the federated Cloud model, where custom virtual machines (VM) are scheduled in appropriate hosts belonging to different providers to execute such experim...

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Autores principales: Pacini, E., Mateos, C., Garcia Garino, C.
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Acceso en línea:http://hdl.handle.net/20.500.12110/paper_15480992_v13_n10_p3359_Pacini
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spelling todo:paper_15480992_v13_n10_p3359_Pacini2023-10-03T16:23:12Z A Three-level Scheduler to Execute Scientific Experiments on Federated Clouds Pacini, E. Mateos, C. Garcia Garino, C. Ant colony optimization Federated Cloud Particle swarm optimization Scheduling Scientific experiments Artificial intelligence Particle swarm optimization (PSO) Scheduling Ant Colony Optimization (ACO) Computing power Federated clouds Network latencies Scientific experiments Simulated experiments Solution path Virtual machines Ant colony optimization For executing current simulated scientific experiments it is necessary to have huge amounts of computing power. A solution path to this problem is the federated Cloud model, where custom virtual machines (VM) are scheduled in appropriate hosts belonging to different providers to execute such experiments, minimizing response time. In this paper, we study schedulers for federated Clouds. Scheduling is performed at three levels. First, at the broker level, datacenters are selected by their network latencies via three policies Lowest-Latency-Time-First, First-Latency-Time-First, and Latency-Time-In-Round. Second, at the infrastructure level, two Cloud VM schedulers based on Ant Colony Optimization (ACO) and Particle Swarm Optimization (PSO) are implemented. At this level the scheduler is responsible for mapping VMs to datacenter hosts. Finally, at the VM level, jobs are assigned for execution into the preallocated VMs. We evaluate, through simulated experiments, how the proposed three-level scheduler performs w.r.t. the response time delivered to the user as the number of Cloud machines increases, a property known as horizontal scalability. © 2015 IEEE. JOUR info:eu-repo/semantics/openAccess http://creativecommons.org/licenses/by/2.5/ar http://hdl.handle.net/20.500.12110/paper_15480992_v13_n10_p3359_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
Federated Cloud
Particle swarm optimization
Scheduling
Scientific experiments
Artificial intelligence
Particle swarm optimization (PSO)
Scheduling
Ant Colony Optimization (ACO)
Computing power
Federated clouds
Network latencies
Scientific experiments
Simulated experiments
Solution path
Virtual machines
Ant colony optimization
spellingShingle Ant colony optimization
Federated Cloud
Particle swarm optimization
Scheduling
Scientific experiments
Artificial intelligence
Particle swarm optimization (PSO)
Scheduling
Ant Colony Optimization (ACO)
Computing power
Federated clouds
Network latencies
Scientific experiments
Simulated experiments
Solution path
Virtual machines
Ant colony optimization
Pacini, E.
Mateos, C.
Garcia Garino, C.
A Three-level Scheduler to Execute Scientific Experiments on Federated Clouds
topic_facet Ant colony optimization
Federated Cloud
Particle swarm optimization
Scheduling
Scientific experiments
Artificial intelligence
Particle swarm optimization (PSO)
Scheduling
Ant Colony Optimization (ACO)
Computing power
Federated clouds
Network latencies
Scientific experiments
Simulated experiments
Solution path
Virtual machines
Ant colony optimization
description For executing current simulated scientific experiments it is necessary to have huge amounts of computing power. A solution path to this problem is the federated Cloud model, where custom virtual machines (VM) are scheduled in appropriate hosts belonging to different providers to execute such experiments, minimizing response time. In this paper, we study schedulers for federated Clouds. Scheduling is performed at three levels. First, at the broker level, datacenters are selected by their network latencies via three policies Lowest-Latency-Time-First, First-Latency-Time-First, and Latency-Time-In-Round. Second, at the infrastructure level, two Cloud VM schedulers based on Ant Colony Optimization (ACO) and Particle Swarm Optimization (PSO) are implemented. At this level the scheduler is responsible for mapping VMs to datacenter hosts. Finally, at the VM level, jobs are assigned for execution into the preallocated VMs. We evaluate, through simulated experiments, how the proposed three-level scheduler performs w.r.t. the response time delivered to the user as the number of Cloud machines increases, a property known as horizontal scalability. © 2015 IEEE.
format JOUR
author Pacini, E.
Mateos, C.
Garcia Garino, C.
author_facet Pacini, E.
Mateos, C.
Garcia Garino, C.
author_sort Pacini, E.
title A Three-level Scheduler to Execute Scientific Experiments on Federated Clouds
title_short A Three-level Scheduler to Execute Scientific Experiments on Federated Clouds
title_full A Three-level Scheduler to Execute Scientific Experiments on Federated Clouds
title_fullStr A Three-level Scheduler to Execute Scientific Experiments on Federated Clouds
title_full_unstemmed A Three-level Scheduler to Execute Scientific Experiments on Federated Clouds
title_sort three-level scheduler to execute scientific experiments on federated clouds
url http://hdl.handle.net/20.500.12110/paper_15480992_v13_n10_p3359_Pacini
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AT garciagarinoc athreelevelschedulertoexecutescientificexperimentsonfederatedclouds
AT pacinie threelevelschedulertoexecutescientificexperimentsonfederatedclouds
AT mateosc threelevelschedulertoexecutescientificexperimentsonfederatedclouds
AT garciagarinoc threelevelschedulertoexecutescientificexperimentsonfederatedclouds
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