Towards virtual machine scheduling research based on multi-decision AHP method in the cloud computing platform
Virtual machine scheduling and resource allocation mechanism in the process of dynamic virtual machine consolidation is a promising access to alleviate the cloud data centers of prominent energy consumption and service level agreement violations with improvement in quality of service (QoS). In this...
| Main Authors: | , , , , , , |
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| Format: | Article |
| Language: | English |
| Published: |
PeerJ Inc.
2023-11-01
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| Series: | PeerJ Computer Science |
| Subjects: | |
| Online Access: | https://peerj.com/articles/cs-1675.pdf |
| _version_ | 1827760929998110720 |
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| author | Hangyu Gu Jinjiang Wang Junyang Yu Dan Wang Bohan Li Xin He Xiang Yin |
| author_facet | Hangyu Gu Jinjiang Wang Junyang Yu Dan Wang Bohan Li Xin He Xiang Yin |
| author_sort | Hangyu Gu |
| collection | DOAJ |
| description | Virtual machine scheduling and resource allocation mechanism in the process of dynamic virtual machine consolidation is a promising access to alleviate the cloud data centers of prominent energy consumption and service level agreement violations with improvement in quality of service (QoS). In this article, we propose an efficient algorithm (AESVMP) based on the Analytic Hierarchy Process (AHP) for the virtual machine scheduling in accordance with the measure. Firstly, we take into consideration three key criteria including the host of power consumption, available resource and resource allocation balance ratio, in which the ratio can be calculated by the balance value between overall three-dimensional resource (CPU, RAM, BW) flat surface and resource allocation flat surface (when new migrated virtual machine (VM) consumed the targeted host’s resource). Then, virtual machine placement decision is determined by the application of multi-criteria decision making techniques AHP embedded with the above-mentioned three criteria. Extensive experimental results based on the CloudSim emulator using 10 PlanetLab workloads demonstrate that the proposed approach can reduce the cloud data center of number of migration, service level agreement violation (SLAV), aggregate indicators of energy comsumption (ESV) by an average of 51.76%, 67.4%, 67.6% compared with the cutting-edge method LBVMP, which validates the effectiveness. |
| first_indexed | 2024-03-11T10:06:06Z |
| format | Article |
| id | doaj.art-a9e078a71d7f4848a17d52ef58007e82 |
| institution | Directory Open Access Journal |
| issn | 2376-5992 |
| language | English |
| last_indexed | 2024-03-11T10:06:06Z |
| publishDate | 2023-11-01 |
| publisher | PeerJ Inc. |
| record_format | Article |
| series | PeerJ Computer Science |
| spelling | doaj.art-a9e078a71d7f4848a17d52ef58007e822023-11-16T15:05:16ZengPeerJ Inc.PeerJ Computer Science2376-59922023-11-019e167510.7717/peerj-cs.1675Towards virtual machine scheduling research based on multi-decision AHP method in the cloud computing platformHangyu GuJinjiang WangJunyang YuDan WangBohan LiXin HeXiang YinVirtual machine scheduling and resource allocation mechanism in the process of dynamic virtual machine consolidation is a promising access to alleviate the cloud data centers of prominent energy consumption and service level agreement violations with improvement in quality of service (QoS). In this article, we propose an efficient algorithm (AESVMP) based on the Analytic Hierarchy Process (AHP) for the virtual machine scheduling in accordance with the measure. Firstly, we take into consideration three key criteria including the host of power consumption, available resource and resource allocation balance ratio, in which the ratio can be calculated by the balance value between overall three-dimensional resource (CPU, RAM, BW) flat surface and resource allocation flat surface (when new migrated virtual machine (VM) consumed the targeted host’s resource). Then, virtual machine placement decision is determined by the application of multi-criteria decision making techniques AHP embedded with the above-mentioned three criteria. Extensive experimental results based on the CloudSim emulator using 10 PlanetLab workloads demonstrate that the proposed approach can reduce the cloud data center of number of migration, service level agreement violation (SLAV), aggregate indicators of energy comsumption (ESV) by an average of 51.76%, 67.4%, 67.6% compared with the cutting-edge method LBVMP, which validates the effectiveness.https://peerj.com/articles/cs-1675.pdfCloud computing platformsQoSAHPVirtual machine placement |
| spellingShingle | Hangyu Gu Jinjiang Wang Junyang Yu Dan Wang Bohan Li Xin He Xiang Yin Towards virtual machine scheduling research based on multi-decision AHP method in the cloud computing platform PeerJ Computer Science Cloud computing platforms QoS AHP Virtual machine placement |
| title | Towards virtual machine scheduling research based on multi-decision AHP method in the cloud computing platform |
| title_full | Towards virtual machine scheduling research based on multi-decision AHP method in the cloud computing platform |
| title_fullStr | Towards virtual machine scheduling research based on multi-decision AHP method in the cloud computing platform |
| title_full_unstemmed | Towards virtual machine scheduling research based on multi-decision AHP method in the cloud computing platform |
| title_short | Towards virtual machine scheduling research based on multi-decision AHP method in the cloud computing platform |
| title_sort | towards virtual machine scheduling research based on multi decision ahp method in the cloud computing platform |
| topic | Cloud computing platforms QoS AHP Virtual machine placement |
| url | https://peerj.com/articles/cs-1675.pdf |
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