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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2015
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| Acceso en línea: | https://bibliotecadigital.exactas.uba.ar/collection/paper/document/paper_15480992_v13_n10_p3359_Pacini http://hdl.handle.net/20.500.12110/paper_15480992_v13_n10_p3359_Pacini |
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paper:paper_15480992_v13_n10_p3359_Pacini |
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paper:paper_15480992_v13_n10_p3359_Pacini2023-06-08T16:21:20Z A Three-level Scheduler to Execute Scientific Experiments on Federated Clouds 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. 2015 https://bibliotecadigital.exactas.uba.ar/collection/paper/document/paper_15480992_v13_n10_p3359_Pacini 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 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. |
| 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 |
| publishDate |
2015 |
| url |
https://bibliotecadigital.exactas.uba.ar/collection/paper/document/paper_15480992_v13_n10_p3359_Pacini http://hdl.handle.net/20.500.12110/paper_15480992_v13_n10_p3359_Pacini |
| _version_ |
1768541577775415296 |