CPU and RAM Energy-Based SLA-Aware Workload Consolidation Techniques for Clouds
Cloud computing offers hardware and software resources delivered as services. It provides solutions for dynamic as well as “pay as you go” provision of resources. Energy consumption of these resources is high which leads to higher operational costs and carbon emissions in data...
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Language: | English |
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IEEE
2020-01-01
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Series: | IEEE Access |
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Online Access: | https://ieeexplore.ieee.org/document/9054962/ |
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author | Beenish Gul Imran Ali Khan Saad Mustafa Osman Khalid Syed Sajid Hussain Darren Dancey Raheel Nawaz |
author_facet | Beenish Gul Imran Ali Khan Saad Mustafa Osman Khalid Syed Sajid Hussain Darren Dancey Raheel Nawaz |
author_sort | Beenish Gul |
collection | DOAJ |
description | Cloud computing offers hardware and software resources delivered as services. It provides solutions for dynamic as well as “pay as you go” provision of resources. Energy consumption of these resources is high which leads to higher operational costs and carbon emissions in data centers. A number of research studies have been conducted on energy efficiency of data centers, but most of them concentrate on single factor energy consumption, i.e., energy consumed by CPU only, and energy consumption by Random Access Memory (RAM) is neglected. However, recently the focus has been turned towards impact of energy consumption by RAM on data centers. Studies have shown that RAM consumes about 25% of joint energy consumed by a server's CPU and RAM. In this paper, two energy-aware virtual machine (VM) consolidation schemes are proposed that take into account a server's capacity in terms of CPU and RAM to reduce the overall energy consumption. The proposed schemes are compared with existing schemes using CloudSim simulator. The results show that the proposed schemes reduce the energy cost with improved Service Level Agreement (SLA). |
first_indexed | 2024-12-23T23:41:16Z |
format | Article |
id | doaj.art-6aa6b4fe9c404959aecabb4738176282 |
institution | Directory Open Access Journal |
issn | 2169-3536 |
language | English |
last_indexed | 2024-12-23T23:41:16Z |
publishDate | 2020-01-01 |
publisher | IEEE |
record_format | Article |
series | IEEE Access |
spelling | doaj.art-6aa6b4fe9c404959aecabb47381762822022-12-21T17:25:39ZengIEEEIEEE Access2169-35362020-01-018629906300310.1109/ACCESS.2020.29852349054962CPU and RAM Energy-Based SLA-Aware Workload Consolidation Techniques for CloudsBeenish Gul0Imran Ali Khan1Saad Mustafa2Osman Khalid3https://orcid.org/0000-0003-4613-6352Syed Sajid Hussain4Darren Dancey5https://orcid.org/0000-0001-7251-8958Raheel Nawaz6https://orcid.org/0000-0001-9588-0052Department of Computer Science, COMSATS University Islamabad, Abbottabad Campus, Abbottabad, PakistanDepartment of Computer Science, COMSATS University Islamabad, Abbottabad Campus, Abbottabad, PakistanDepartment of Computer Science, COMSATS University Islamabad, Abbottabad Campus, Abbottabad, PakistanDepartment of Computer Science, COMSATS University Islamabad, Abbottabad Campus, Abbottabad, PakistanDepartment of Computer Science, COMSATS University Islamabad, Abbottabad Campus, Abbottabad, PakistanDepartment of Computing and Mathematics, Manchester Metropolitan University, Manchester, U.KDepartment of Operations, Technology Events, and Hospitality Management Manchester, Metropolitan University, Manchester, U.K.Cloud computing offers hardware and software resources delivered as services. It provides solutions for dynamic as well as “pay as you go” provision of resources. Energy consumption of these resources is high which leads to higher operational costs and carbon emissions in data centers. A number of research studies have been conducted on energy efficiency of data centers, but most of them concentrate on single factor energy consumption, i.e., energy consumed by CPU only, and energy consumption by Random Access Memory (RAM) is neglected. However, recently the focus has been turned towards impact of energy consumption by RAM on data centers. Studies have shown that RAM consumes about 25% of joint energy consumed by a server's CPU and RAM. In this paper, two energy-aware virtual machine (VM) consolidation schemes are proposed that take into account a server's capacity in terms of CPU and RAM to reduce the overall energy consumption. The proposed schemes are compared with existing schemes using CloudSim simulator. The results show that the proposed schemes reduce the energy cost with improved Service Level Agreement (SLA).https://ieeexplore.ieee.org/document/9054962/Cloud computingenergy efficiencymulti-factor energy consumptionresource allocationvirtualizationworkload consolidation |
spellingShingle | Beenish Gul Imran Ali Khan Saad Mustafa Osman Khalid Syed Sajid Hussain Darren Dancey Raheel Nawaz CPU and RAM Energy-Based SLA-Aware Workload Consolidation Techniques for Clouds IEEE Access Cloud computing energy efficiency multi-factor energy consumption resource allocation virtualization workload consolidation |
title | CPU and RAM Energy-Based SLA-Aware Workload Consolidation Techniques for Clouds |
title_full | CPU and RAM Energy-Based SLA-Aware Workload Consolidation Techniques for Clouds |
title_fullStr | CPU and RAM Energy-Based SLA-Aware Workload Consolidation Techniques for Clouds |
title_full_unstemmed | CPU and RAM Energy-Based SLA-Aware Workload Consolidation Techniques for Clouds |
title_short | CPU and RAM Energy-Based SLA-Aware Workload Consolidation Techniques for Clouds |
title_sort | cpu and ram energy based sla aware workload consolidation techniques for clouds |
topic | Cloud computing energy efficiency multi-factor energy consumption resource allocation virtualization workload consolidation |
url | https://ieeexplore.ieee.org/document/9054962/ |
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