2019
Cesario, E.; Vinci, A.
A comparative analysis of classification and regression models for energy-efficient clouds Proceedings Article
In: 2019 IEEE 16th International Conference on Networking, Sensing and Control (ICNSC), IEEE, 2019, ISBN: 9781728100838.
Abstract | Links | BibTeX | Tag: Data Mining for Energy Efficiency, Energy-aware Clouds, Green Computing
@inproceedings{Cesario2019,
title = {A comparative analysis of classification and regression models for energy-efficient clouds},
author = {E. Cesario and A. Vinci},
doi = {10.1109/ICNSC.2019.8743292},
isbn = {9781728100838},
year = {2019},
date = {2019-01-01},
urldate = {2019-01-01},
booktitle = {2019 IEEE 16th International Conference on Networking, Sensing and Control (ICNSC)},
journal = {Proceedings of the 2019 IEEE 16th International Conference on Networking, Sensing and Control, ICNSC 2019},
publisher = {IEEE},
abstract = {Consolidation of virtual machines (VM) is one of the key strategies used to reduce the power consumption of Cloud servers. For this reason, it is extensively studied. Consolidation has the goal of allocating virtual machines on a few physical servers as possible while satisfying the Service Level Agreement established with users. Nevertheless, the effectiveness of a con-solidation strategy strongly depends on the forecast of the VMs resource needs. This paper presents the experimental evaluation of a system for energy-aware allocation of virtual machines, driven by predictive data mining models. Migrations are driven by the forecast of the future computational needs (CPU, RAM) of each virtual machine, in order to efficiently allocate those on the available servers. The experimental evaluation, performed on real-world Cloud data traces, reports a comparison of performance achieved by exploiting classification and regression models and shows good benefits in terms of energy saving.},
keywords = {Data Mining for Energy Efficiency, Energy-aware Clouds, Green Computing},
pubstate = {published},
tppubtype = {inproceedings}
}
Consolidation of virtual machines (VM) is one of the key strategies used to reduce the power consumption of Cloud servers. For this reason, it is extensively studied. Consolidation has the goal of allocating virtual machines on a few physical servers as possible while satisfying the Service Level Agreement established with users. Nevertheless, the effectiveness of a con-solidation strategy strongly depends on the forecast of the VMs resource needs. This paper presents the experimental evaluation of a system for energy-aware allocation of virtual machines, driven by predictive data mining models. Migrations are driven by the forecast of the future computational needs (CPU, RAM) of each virtual machine, in order to efficiently allocate those on the available servers. The experimental evaluation, performed on real-world Cloud data traces, reports a comparison of performance achieved by exploiting classification and regression models and shows good benefits in terms of energy saving.
Altomare, A.; Cesario, E.; Vinci, A.
Data analytics for energy-efficient clouds: design, implementation and evaluation Journal Article
In: International Journal of Parallel, Emergent and Distributed Systems, vol. 34, iss. 6, 2019, ISSN: 17445779.
Abstract | Links | BibTeX | Tag: Data Mining for Energy Efficiency, Energy-aware Clouds, Green Computing
@article{Altomare2019,
title = {Data analytics for energy-efficient clouds: design, implementation and evaluation},
author = {A. Altomare and E. Cesario and A. Vinci},
doi = {10.1080/17445760.2018.1448931},
issn = {17445779},
year = {2019},
date = {2019-01-01},
journal = {International Journal of Parallel, Emergent and Distributed Systems},
volume = {34},
issue = {6},
abstract = {The success of Cloud Computing and the resulting ever growing of large data centers is causing a huge rise in electrical power consumption by hardware facilities and cooling systems. This results in an increment of operational costs of data centres, that is becoming a crucial issue to deal with. Consolidation of virtual machines (VM) is one of the key strategies used to reduce the power consumption of Cloud servers. For this reason, it is extensively studied. Consolidation has the goal of allocating virtual machines on a few physical servers as possible while satisfying the Service Level Agreement established with users. Nevertheless, the effectiveness of a consolidation strategy strongly depends on the forecast of the VM resource needs. Predictive data mining models can be exploited for this purpose. This paper describes the design and development of a system for energy-aware allocation of virtual machines, driven by predictive data mining models. In particular, migrations are driven by the forecast of the future computational needs (CPU, RAM) of each virtual machine, in order to efficiently allocate those on the available servers. The experimental evaluation, performed on real-world Cloud data traces, reports a comparison of performance achieved by exploiting several classification models and shows good benefits in terms of energy saving.},
keywords = {Data Mining for Energy Efficiency, Energy-aware Clouds, Green Computing},
pubstate = {published},
tppubtype = {article}
}
The success of Cloud Computing and the resulting ever growing of large data centers is causing a huge rise in electrical power consumption by hardware facilities and cooling systems. This results in an increment of operational costs of data centres, that is becoming a crucial issue to deal with. Consolidation of virtual machines (VM) is one of the key strategies used to reduce the power consumption of Cloud servers. For this reason, it is extensively studied. Consolidation has the goal of allocating virtual machines on a few physical servers as possible while satisfying the Service Level Agreement established with users. Nevertheless, the effectiveness of a consolidation strategy strongly depends on the forecast of the VM resource needs. Predictive data mining models can be exploited for this purpose. This paper describes the design and development of a system for energy-aware allocation of virtual machines, driven by predictive data mining models. In particular, migrations are driven by the forecast of the future computational needs (CPU, RAM) of each virtual machine, in order to efficiently allocate those on the available servers. The experimental evaluation, performed on real-world Cloud data traces, reports a comparison of performance achieved by exploiting several classification models and shows good benefits in terms of energy saving.