Dependency Analysis based Approach for Virtual Machine Placement in Software-Defined Data Center
In data centers, cloud-based services are usually deployed among multiple virtual machines (VMs), and these VMs have data traffic dependencies on each other. However, traffic dependency between VMs has not been fully considered when the services running in the data center are expanded by creating ad...
| Main Authors: | , , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
MDPI AG
2019-08-01
|
| Series: | Applied Sciences |
| Subjects: | |
| Online Access: | https://www.mdpi.com/2076-3417/9/16/3223 |
| _version_ | 1818277586993676288 |
|---|---|
| author | Jargalsaikhan Narantuya Taejin Ha Jaewon Bae Hyuk Lim |
| author_facet | Jargalsaikhan Narantuya Taejin Ha Jaewon Bae Hyuk Lim |
| author_sort | Jargalsaikhan Narantuya |
| collection | DOAJ |
| description | In data centers, cloud-based services are usually deployed among multiple virtual machines (VMs), and these VMs have data traffic dependencies on each other. However, traffic dependency between VMs has not been fully considered when the services running in the data center are expanded by creating additional VMs. If highly dependent VMs are placed in different physical machines (PMs), the data traffic increases in the underlying physical network of the data center. To reduce the amount of data traffic in the underlying network and improve the service performance, we propose a traffic-dependency-based strategy for VM placement in software-defined data center (SDDC). The traffic dependencies between the VMs are analyzed by principal component analysis, and highly dependent VMs are grouped by gravity-based clustering. Each group of highly dependent VMs is placed within an appropriate PM based on the Hungarian matching method. This strategy of dependency-based VM placement facilitates reducing data traffic volume of the data center, since the highly dependent VMs are placed within the same PM. The results of the performance evaluation in SDDC testbed indicate that the proposed VM placement method efficiently reduces the amount of data traffic in the underlying network and improves the data center performance. |
| first_indexed | 2024-12-12T23:03:54Z |
| format | Article |
| id | doaj.art-51516827ead64ead8eac04327e40c82b |
| institution | Directory Open Access Journal |
| issn | 2076-3417 |
| language | English |
| last_indexed | 2024-12-12T23:03:54Z |
| publishDate | 2019-08-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Applied Sciences |
| spelling | doaj.art-51516827ead64ead8eac04327e40c82b2022-12-22T00:08:45ZengMDPI AGApplied Sciences2076-34172019-08-01916322310.3390/app9163223app9163223Dependency Analysis based Approach for Virtual Machine Placement in Software-Defined Data CenterJargalsaikhan Narantuya0Taejin Ha1Jaewon Bae2Hyuk Lim3School of Electrical Engineering and Computer Science, Gwangju Institute of Science and Technology (GIST), Gwangju 61005, KoreaLG Electronics, Seoul 07329, KoreaSchool of Electrical Engineering and Computer Science, Gwangju Institute of Science and Technology (GIST), Gwangju 61005, KoreaSchool of Electrical Engineering and Computer Science, Gwangju Institute of Science and Technology (GIST), Gwangju 61005, KoreaIn data centers, cloud-based services are usually deployed among multiple virtual machines (VMs), and these VMs have data traffic dependencies on each other. However, traffic dependency between VMs has not been fully considered when the services running in the data center are expanded by creating additional VMs. If highly dependent VMs are placed in different physical machines (PMs), the data traffic increases in the underlying physical network of the data center. To reduce the amount of data traffic in the underlying network and improve the service performance, we propose a traffic-dependency-based strategy for VM placement in software-defined data center (SDDC). The traffic dependencies between the VMs are analyzed by principal component analysis, and highly dependent VMs are grouped by gravity-based clustering. Each group of highly dependent VMs is placed within an appropriate PM based on the Hungarian matching method. This strategy of dependency-based VM placement facilitates reducing data traffic volume of the data center, since the highly dependent VMs are placed within the same PM. The results of the performance evaluation in SDDC testbed indicate that the proposed VM placement method efficiently reduces the amount of data traffic in the underlying network and improves the data center performance.https://www.mdpi.com/2076-3417/9/16/3223scalable VM placementsoftware-defined data centertraffic dependencydependency analysis |
| spellingShingle | Jargalsaikhan Narantuya Taejin Ha Jaewon Bae Hyuk Lim Dependency Analysis based Approach for Virtual Machine Placement in Software-Defined Data Center Applied Sciences scalable VM placement software-defined data center traffic dependency dependency analysis |
| title | Dependency Analysis based Approach for Virtual Machine Placement in Software-Defined Data Center |
| title_full | Dependency Analysis based Approach for Virtual Machine Placement in Software-Defined Data Center |
| title_fullStr | Dependency Analysis based Approach for Virtual Machine Placement in Software-Defined Data Center |
| title_full_unstemmed | Dependency Analysis based Approach for Virtual Machine Placement in Software-Defined Data Center |
| title_short | Dependency Analysis based Approach for Virtual Machine Placement in Software-Defined Data Center |
| title_sort | dependency analysis based approach for virtual machine placement in software defined data center |
| topic | scalable VM placement software-defined data center traffic dependency dependency analysis |
| url | https://www.mdpi.com/2076-3417/9/16/3223 |
| work_keys_str_mv | AT jargalsaikhannarantuya dependencyanalysisbasedapproachforvirtualmachineplacementinsoftwaredefineddatacenter AT taejinha dependencyanalysisbasedapproachforvirtualmachineplacementinsoftwaredefineddatacenter AT jaewonbae dependencyanalysisbasedapproachforvirtualmachineplacementinsoftwaredefineddatacenter AT hyuklim dependencyanalysisbasedapproachforvirtualmachineplacementinsoftwaredefineddatacenter |