Two-Phase Virtual Machine Placement Algorithms for Cloud Computing: An Experimental Evaluation under Uncertainty
Cloud computing providers must support requests for resources in dynamic environments, considering service elasticity and overbooking of physical resources. Due to the randomness of requests, Virtual Machine Placement (VMP) problems should be formulated under uncertainty. In this context, a renewed...
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| Autores principales: | , , |
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| Formato: | Objeto de conferencia |
| Lenguaje: | Inglés |
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2017
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| Acceso en línea: | http://sedici.unlp.edu.ar/handle/10915/65519 |
| Aporte de: |
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I19-R120-10915-65519 |
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| record_format |
dspace |
| institution |
Universidad Nacional de La Plata |
| institution_str |
I-19 |
| repository_str |
R-120 |
| collection |
SEDICI (UNLP) |
| language |
Inglés |
| topic |
Ciencias Informáticas virtual machine placement cloud computing overbooking elasticity uncertainty incremental vmp vmp reconfiguration |
| spellingShingle |
Ciencias Informáticas virtual machine placement cloud computing overbooking elasticity uncertainty incremental vmp vmp reconfiguration Chamas, Nabil López-Pires, Fabio Barán, Benjamín Two-Phase Virtual Machine Placement Algorithms for Cloud Computing: An Experimental Evaluation under Uncertainty |
| topic_facet |
Ciencias Informáticas virtual machine placement cloud computing overbooking elasticity uncertainty incremental vmp vmp reconfiguration |
| description |
Cloud computing providers must support requests for resources in dynamic environments, considering service elasticity and overbooking of physical resources. Due to the randomness of requests, Virtual Machine Placement (VMP) problems should be formulated under uncertainty. In this context, a renewed formulation of the VMP problem is presented, considering the optimization of four objective functions:
(i) power consumption, (ii) economical revenue, (iii) resource utilization and (iv) reconfiguration time. To solve the presented formulation, a two-phase optimization scheme is considered, composed by an online incremental VMP phase (iVMP) and an offline VMP reconfiguration (VMPr) phase. An experimental evaluation of five algorithms taking into account 400 different scenarios was performed, considering three VMPr Triggering and two VMPr Recovering methods as well as three VMPr resolution alternatives. Experimental results indicate which algorithm outperformed the other evaluated algorithms, improving the quality of solutions in a scenario-based uncertainty model considering the following evaluation criteria: (i) average, (ii) maximum and (iii) minimum objective function costs. |
| format |
Objeto de conferencia Objeto de conferencia |
| author |
Chamas, Nabil López-Pires, Fabio Barán, Benjamín |
| author_facet |
Chamas, Nabil López-Pires, Fabio Barán, Benjamín |
| author_sort |
Chamas, Nabil |
| title |
Two-Phase Virtual Machine Placement Algorithms for Cloud Computing: An Experimental Evaluation under Uncertainty |
| title_short |
Two-Phase Virtual Machine Placement Algorithms for Cloud Computing: An Experimental Evaluation under Uncertainty |
| title_full |
Two-Phase Virtual Machine Placement Algorithms for Cloud Computing: An Experimental Evaluation under Uncertainty |
| title_fullStr |
Two-Phase Virtual Machine Placement Algorithms for Cloud Computing: An Experimental Evaluation under Uncertainty |
| title_full_unstemmed |
Two-Phase Virtual Machine Placement Algorithms for Cloud Computing: An Experimental Evaluation under Uncertainty |
| title_sort |
two-phase virtual machine placement algorithms for cloud computing: an experimental evaluation under uncertainty |
| publishDate |
2017 |
| url |
http://sedici.unlp.edu.ar/handle/10915/65519 |
| work_keys_str_mv |
AT chamasnabil twophasevirtualmachineplacementalgorithmsforcloudcomputinganexperimentalevaluationunderuncertainty AT lopezpiresfabio twophasevirtualmachineplacementalgorithmsforcloudcomputinganexperimentalevaluationunderuncertainty AT baranbenjamin twophasevirtualmachineplacementalgorithmsforcloudcomputinganexperimentalevaluationunderuncertainty |
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Repositorios |
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1764820480705953797 |