EAMA: Efficient Adaptive Migration Algorithm for Cloud Data Centers (CDCs)
The rapid demand for Cloud services resulted in the establishment of large-scale Cloud Data Centers (CDCs), which ultimately consume a large amount of energy. An enormous amount of energy consumption eventually leads to high operating costs and carbon emissions. To reduce energy consumption with eff...
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| Format: | Article |
| Language: | English |
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MDPI AG
2021-04-01
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| Series: | Symmetry |
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| Online Access: | https://www.mdpi.com/2073-8994/13/4/690 |
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| author | Muhammad Ibrahim Muhammad Imran Faisal Jamil Yun-Jung Lee Do-Hyeun Kim |
| author_facet | Muhammad Ibrahim Muhammad Imran Faisal Jamil Yun-Jung Lee Do-Hyeun Kim |
| author_sort | Muhammad Ibrahim |
| collection | DOAJ |
| description | The rapid demand for Cloud services resulted in the establishment of large-scale Cloud Data Centers (CDCs), which ultimately consume a large amount of energy. An enormous amount of energy consumption eventually leads to high operating costs and carbon emissions. To reduce energy consumption with efficient resource utilization, various dynamic Virtual Machine (VM) consolidation approaches (i.e., Predictive Anti-Correlated Placement Algorithm (PACPA), Resource-Utilization-Aware Energy Efficient (RUAEE), Memory-bound Pre-copy Live Migration (MPLM), m Mixed migration strategy, Memory/disk operation aware Live VM Migration (MLLM), etc.) have been considered. Most of these techniques do aggressive VM consolidation that eventually results in performance degradation of CDCs in terms of resource utilization and energy consumption. In this paper, an Efficient Adaptive Migration Algorithm (EAMA) is proposed for effective migration and placement of VMs on the Physical Machines (PMs) dynamically. The proposed approach has two distinct features: first, selection of PM locations with optimum access delay where the VMs are required to be migrated, and second, reduces the number of VM migrations. Extensive simulation experiments have been conducted using the CloudSim toolkit. The results of the proposed approach are compared with the PACPA and RUAEE algorithms in terms of Service-Level Agreement (SLA) violation, resource utilization, number of hosts shut down, and energy consumption. Results show that proposed EAMA approach significantly reduces the number of migrations by 16% and 24%, SLA violation by 20% and 34%, and increases the resource utilization by 8% to 17% with increased number of hosts shut down from 10% to 13% as compared to the PACPA and RUAEE, respectively. Moreover, a 13% improvement in energy consumption has also been observed. |
| first_indexed | 2024-03-10T12:17:47Z |
| format | Article |
| id | doaj.art-dbc34026595847d186047c9ebec0c4e0 |
| institution | Directory Open Access Journal |
| issn | 2073-8994 |
| language | English |
| last_indexed | 2024-03-10T12:17:47Z |
| publishDate | 2021-04-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Symmetry |
| spelling | doaj.art-dbc34026595847d186047c9ebec0c4e02023-11-21T15:44:42ZengMDPI AGSymmetry2073-89942021-04-0113469010.3390/sym13040690EAMA: Efficient Adaptive Migration Algorithm for Cloud Data Centers (CDCs)Muhammad Ibrahim0Muhammad Imran1Faisal Jamil2Yun-Jung Lee3Do-Hyeun Kim4Department of Computer Engineering, Jeju National University, Jeju 63243, KoreaDepartment of Electronics and Computer Engineering, Hongik University Sejong Campus, Sejong 2639, KoreaDepartment of Computer Engineering, Jeju National University, Jeju 63243, KoreaDepartment of Computer Science and Statistics, Jeju National University, Jeju 63243, KoreaDepartment of Computer Engineering, Jeju National University, Jeju 63243, KoreaThe rapid demand for Cloud services resulted in the establishment of large-scale Cloud Data Centers (CDCs), which ultimately consume a large amount of energy. An enormous amount of energy consumption eventually leads to high operating costs and carbon emissions. To reduce energy consumption with efficient resource utilization, various dynamic Virtual Machine (VM) consolidation approaches (i.e., Predictive Anti-Correlated Placement Algorithm (PACPA), Resource-Utilization-Aware Energy Efficient (RUAEE), Memory-bound Pre-copy Live Migration (MPLM), m Mixed migration strategy, Memory/disk operation aware Live VM Migration (MLLM), etc.) have been considered. Most of these techniques do aggressive VM consolidation that eventually results in performance degradation of CDCs in terms of resource utilization and energy consumption. In this paper, an Efficient Adaptive Migration Algorithm (EAMA) is proposed for effective migration and placement of VMs on the Physical Machines (PMs) dynamically. The proposed approach has two distinct features: first, selection of PM locations with optimum access delay where the VMs are required to be migrated, and second, reduces the number of VM migrations. Extensive simulation experiments have been conducted using the CloudSim toolkit. The results of the proposed approach are compared with the PACPA and RUAEE algorithms in terms of Service-Level Agreement (SLA) violation, resource utilization, number of hosts shut down, and energy consumption. Results show that proposed EAMA approach significantly reduces the number of migrations by 16% and 24%, SLA violation by 20% and 34%, and increases the resource utilization by 8% to 17% with increased number of hosts shut down from 10% to 13% as compared to the PACPA and RUAEE, respectively. Moreover, a 13% improvement in energy consumption has also been observed.https://www.mdpi.com/2073-8994/13/4/690VM migrationVM placementCloudSimcloud computingserver consolidation |
| spellingShingle | Muhammad Ibrahim Muhammad Imran Faisal Jamil Yun-Jung Lee Do-Hyeun Kim EAMA: Efficient Adaptive Migration Algorithm for Cloud Data Centers (CDCs) Symmetry VM migration VM placement CloudSim cloud computing server consolidation |
| title | EAMA: Efficient Adaptive Migration Algorithm for Cloud Data Centers (CDCs) |
| title_full | EAMA: Efficient Adaptive Migration Algorithm for Cloud Data Centers (CDCs) |
| title_fullStr | EAMA: Efficient Adaptive Migration Algorithm for Cloud Data Centers (CDCs) |
| title_full_unstemmed | EAMA: Efficient Adaptive Migration Algorithm for Cloud Data Centers (CDCs) |
| title_short | EAMA: Efficient Adaptive Migration Algorithm for Cloud Data Centers (CDCs) |
| title_sort | eama efficient adaptive migration algorithm for cloud data centers cdcs |
| topic | VM migration VM placement CloudSim cloud computing server consolidation |
| url | https://www.mdpi.com/2073-8994/13/4/690 |
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