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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Main Authors: Beenish Gul, Imran Ali Khan, Saad Mustafa, Osman Khalid, Syed Sajid Hussain, Darren Dancey, Raheel Nawaz
Format: Article
Language:English
Published: IEEE 2020-01-01
Series:IEEE Access
Subjects:
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).
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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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