Carbon-Efficient Virtual Machine Placement Based on Dynamic Voltage Frequency Scaling in Geo-Distributed Cloud Data Centers
The tremendous growth of big data analysis and IoT (Internet of Things) has made cloud computing an integral part of society. The prominent problem associated with data centers is the growing energy consumption, which results in environmental pollution. Data centers can reduce their carbon emissions...
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MDPI AG
2020-04-01
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| Series: | Applied Sciences |
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| Online Access: | https://www.mdpi.com/2076-3417/10/8/2701 |
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| author | T. Renugadevi K. Geetha Natarajan Prabaharan Pierluigi Siano |
| author_facet | T. Renugadevi K. Geetha Natarajan Prabaharan Pierluigi Siano |
| author_sort | T. Renugadevi |
| collection | DOAJ |
| description | The tremendous growth of big data analysis and IoT (Internet of Things) has made cloud computing an integral part of society. The prominent problem associated with data centers is the growing energy consumption, which results in environmental pollution. Data centers can reduce their carbon emissions through efficient management of server power consumption for a given workload. Dynamic voltage frequency scaling (DVFS) can be applied to control the operating frequencies of the servers based on the workloads assigned to them, as this approach has a cubic increment relationship with power consumption. This research work proposes two DVFS-enabled host selection algorithms for virtual machine (VM) placement with a cluster selection strategy, namely the carbon and power-efficient optimal frequency (C-PEF) algorithm and the carbon-aware first-fit optimal frequency (C-FFF) algorithm.The main aims of the proposed algorithms are to balance the load among the servers and dynamically tune the cooling load based on the current workload. The cluster selection strategy is based on static and dynamic power usage effectiveness (PUE) values and the carbon footprint rate (CFR). The cluster selection is also extended to non-DVFS host selection policies, namely the carbon- and power-efficient (C-PE) algorithm, carbon-aware first-fit (C-FF) algorithm, and carbon-aware first-fit least-empty (C-FFLE) algorithm. The results show that C-FFF achieves 2% more power reduction than C-PEF and C-PE, and demonstrates itself as a power-efficient algorithm for CO<sub>2</sub> reduction, retaining the same quality of service (QoS) as its counterparts with lower computational overheads. |
| first_indexed | 2024-03-10T20:29:19Z |
| format | Article |
| id | doaj.art-c0c3833484dd4b778ddb5b4cb5298914 |
| institution | Directory Open Access Journal |
| issn | 2076-3417 |
| language | English |
| last_indexed | 2024-03-10T20:29:19Z |
| publishDate | 2020-04-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Applied Sciences |
| spelling | doaj.art-c0c3833484dd4b778ddb5b4cb52989142023-11-19T21:32:56ZengMDPI AGApplied Sciences2076-34172020-04-01108270110.3390/app10082701Carbon-Efficient Virtual Machine Placement Based on Dynamic Voltage Frequency Scaling in Geo-Distributed Cloud Data CentersT. Renugadevi0K. Geetha1Natarajan Prabaharan2Pierluigi Siano3School of Computing, SASTRA Deemed University, Thanjavur 613401, IndiaSchool of Computing, SASTRA Deemed University, Thanjavur 613401, IndiaSchool of Electrical and Electronics Engineering, SASTRA Deemed University, Thanjavur 613401, IndiaDepartment of Management & Innovation Systems, University of Salerno, 84084 Fisciano (SA), ItalyThe tremendous growth of big data analysis and IoT (Internet of Things) has made cloud computing an integral part of society. The prominent problem associated with data centers is the growing energy consumption, which results in environmental pollution. Data centers can reduce their carbon emissions through efficient management of server power consumption for a given workload. Dynamic voltage frequency scaling (DVFS) can be applied to control the operating frequencies of the servers based on the workloads assigned to them, as this approach has a cubic increment relationship with power consumption. This research work proposes two DVFS-enabled host selection algorithms for virtual machine (VM) placement with a cluster selection strategy, namely the carbon and power-efficient optimal frequency (C-PEF) algorithm and the carbon-aware first-fit optimal frequency (C-FFF) algorithm.The main aims of the proposed algorithms are to balance the load among the servers and dynamically tune the cooling load based on the current workload. The cluster selection strategy is based on static and dynamic power usage effectiveness (PUE) values and the carbon footprint rate (CFR). The cluster selection is also extended to non-DVFS host selection policies, namely the carbon- and power-efficient (C-PE) algorithm, carbon-aware first-fit (C-FF) algorithm, and carbon-aware first-fit least-empty (C-FFLE) algorithm. The results show that C-FFF achieves 2% more power reduction than C-PEF and C-PE, and demonstrates itself as a power-efficient algorithm for CO<sub>2</sub> reduction, retaining the same quality of service (QoS) as its counterparts with lower computational overheads.https://www.mdpi.com/2076-3417/10/8/2701cloud computingdynamic voltage frequency scalingvirtual machine allocationenergy-efficientcarbon footprint ratepower usage effectiveness |
| spellingShingle | T. Renugadevi K. Geetha Natarajan Prabaharan Pierluigi Siano Carbon-Efficient Virtual Machine Placement Based on Dynamic Voltage Frequency Scaling in Geo-Distributed Cloud Data Centers Applied Sciences cloud computing dynamic voltage frequency scaling virtual machine allocation energy-efficient carbon footprint rate power usage effectiveness |
| title | Carbon-Efficient Virtual Machine Placement Based on Dynamic Voltage Frequency Scaling in Geo-Distributed Cloud Data Centers |
| title_full | Carbon-Efficient Virtual Machine Placement Based on Dynamic Voltage Frequency Scaling in Geo-Distributed Cloud Data Centers |
| title_fullStr | Carbon-Efficient Virtual Machine Placement Based on Dynamic Voltage Frequency Scaling in Geo-Distributed Cloud Data Centers |
| title_full_unstemmed | Carbon-Efficient Virtual Machine Placement Based on Dynamic Voltage Frequency Scaling in Geo-Distributed Cloud Data Centers |
| title_short | Carbon-Efficient Virtual Machine Placement Based on Dynamic Voltage Frequency Scaling in Geo-Distributed Cloud Data Centers |
| title_sort | carbon efficient virtual machine placement based on dynamic voltage frequency scaling in geo distributed cloud data centers |
| topic | cloud computing dynamic voltage frequency scaling virtual machine allocation energy-efficient carbon footprint rate power usage effectiveness |
| url | https://www.mdpi.com/2076-3417/10/8/2701 |
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