A machine learning-based optimization approach for pre-copy live virtual machine migration
<p dir="ltr">Organizations widely use cloud computing to outsource their computing needs. One crucial issue of cloud computing is that services must be available to clients at all times. However, the cloud services may be temporarily unavailable due to maintenance of the cloud infras...
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| مؤلفون آخرون: | , , |
| منشور في: |
2023
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| _version_ | 1864513521129095168 |
|---|---|
| author | Raseena M. Haris (17773470) |
| author2 | Khaled M. Khan (16888788) Armstrong Nhlabatsi (17773473) Mahmoud Barhamgi (12618205) |
| author2_role | author author author |
| author_facet | Raseena M. Haris (17773470) Khaled M. Khan (16888788) Armstrong Nhlabatsi (17773473) Mahmoud Barhamgi (12618205) |
| author_role | author |
| dc.creator.none.fl_str_mv | Raseena M. Haris (17773470) Khaled M. Khan (16888788) Armstrong Nhlabatsi (17773473) Mahmoud Barhamgi (12618205) |
| dc.date.none.fl_str_mv | 2023-05-09T03:00:00Z |
| dc.identifier.none.fl_str_mv | 10.1007/s10586-023-04001-1 |
| dc.relation.none.fl_str_mv | https://figshare.com/articles/journal_contribution/A_machine_learning-based_optimization_approach_for_pre-copy_live_virtual_machine_migration/24981201 |
| dc.rights.none.fl_str_mv | CC BY 4.0 info:eu-repo/semantics/openAccess |
| dc.subject.none.fl_str_mv | Information and computing sciences Distributed computing and systems software Machine learning Cloud computing Live virtual machine migration Pre-copy Dirty page rate Machine learning Feature selection |
| dc.title.none.fl_str_mv | A machine learning-based optimization approach for pre-copy live virtual machine migration |
| dc.type.none.fl_str_mv | Text Journal contribution info:eu-repo/semantics/publishedVersion text contribution to journal |
| description | <p dir="ltr">Organizations widely use cloud computing to outsource their computing needs. One crucial issue of cloud computing is that services must be available to clients at all times. However, the cloud services may be temporarily unavailable due to maintenance of the cloud infrastructure, load balancing of services, defense against cyber attacks, power management, proactive fault tolerance, or resource usage. The unavailability of cloud services impacts negatively on the business model of cloud providers. One solution to tackle the service unavailability is Live Virtual Machine Migration (LVM), that is, moving virtual machines (VMs) from the source host machine to the destination host without disrupting the running application. Pre-copy memory migration is a common LVM approach used in most networked systems such as the cloud. The main difficulty with this approach is the high rate of frequently updating memory pages, referred to as "dirty pages. Transferring these updated or dirty pages during the pre-copy migration approach prolongs the total migration time. After a predefined iteration, the pre-copy approach enters the stop-and-copy phase and transfers the remaining memory pages. If the remaining pages are huge, the downtime or service unavailability will be very high -resulting in a negative impact on the availability of the running services. To minimize such service downtime, it is critical to find an optimal time to migrate a virtual machine in the pre-copy approach. To address the issue, this paper proposes a machine learning-based method to optimize pre-copy migration. It has mainly three stages (i) Feature selection (ii) Model generation and (iii) Application of the proposed model in pre-copy migration. The experiment results show that our proposed model outperforms other machine learning models in terms of prediction accuracy and it significantly reduces downtime or service unavailability during the migration process.</p><h2>Other Information</h2><p dir="ltr">Published in: Cluster Computing<br>License: <a href="https://creativecommons.org/licenses/by/4.0" target="_blank">https://creativecommons.org/licenses/by/4.0</a><br>See article on publisher's website: <a href="https://dx.doi.org/10.1007/s10586-023-04001-1" target="_blank">https://dx.doi.org/10.1007/s10586-023-04001-1</a></p> |
| eu_rights_str_mv | openAccess |
| id | Manara2_1f7bb61747fa698d4c8434ce302aedcc |
| identifier_str_mv | 10.1007/s10586-023-04001-1 |
| network_acronym_str | Manara2 |
| network_name_str | Manara2 |
| oai_identifier_str | oai:figshare.com:article/24981201 |
| publishDate | 2023 |
| repository.mail.fl_str_mv | |
| repository.name.fl_str_mv | |
| repository_id_str | |
| rights_invalid_str_mv | CC BY 4.0 |
| spelling | A machine learning-based optimization approach for pre-copy live virtual machine migrationRaseena M. Haris (17773470)Khaled M. Khan (16888788)Armstrong Nhlabatsi (17773473)Mahmoud Barhamgi (12618205)Information and computing sciencesDistributed computing and systems softwareMachine learningCloud computingLive virtual machine migrationPre-copyDirty page rateMachine learningFeature selection<p dir="ltr">Organizations widely use cloud computing to outsource their computing needs. One crucial issue of cloud computing is that services must be available to clients at all times. However, the cloud services may be temporarily unavailable due to maintenance of the cloud infrastructure, load balancing of services, defense against cyber attacks, power management, proactive fault tolerance, or resource usage. The unavailability of cloud services impacts negatively on the business model of cloud providers. One solution to tackle the service unavailability is Live Virtual Machine Migration (LVM), that is, moving virtual machines (VMs) from the source host machine to the destination host without disrupting the running application. Pre-copy memory migration is a common LVM approach used in most networked systems such as the cloud. The main difficulty with this approach is the high rate of frequently updating memory pages, referred to as "dirty pages. Transferring these updated or dirty pages during the pre-copy migration approach prolongs the total migration time. After a predefined iteration, the pre-copy approach enters the stop-and-copy phase and transfers the remaining memory pages. If the remaining pages are huge, the downtime or service unavailability will be very high -resulting in a negative impact on the availability of the running services. To minimize such service downtime, it is critical to find an optimal time to migrate a virtual machine in the pre-copy approach. To address the issue, this paper proposes a machine learning-based method to optimize pre-copy migration. It has mainly three stages (i) Feature selection (ii) Model generation and (iii) Application of the proposed model in pre-copy migration. The experiment results show that our proposed model outperforms other machine learning models in terms of prediction accuracy and it significantly reduces downtime or service unavailability during the migration process.</p><h2>Other Information</h2><p dir="ltr">Published in: Cluster Computing<br>License: <a href="https://creativecommons.org/licenses/by/4.0" target="_blank">https://creativecommons.org/licenses/by/4.0</a><br>See article on publisher's website: <a href="https://dx.doi.org/10.1007/s10586-023-04001-1" target="_blank">https://dx.doi.org/10.1007/s10586-023-04001-1</a></p>2023-05-09T03:00:00ZTextJournal contributioninfo:eu-repo/semantics/publishedVersiontextcontribution to journal10.1007/s10586-023-04001-1https://figshare.com/articles/journal_contribution/A_machine_learning-based_optimization_approach_for_pre-copy_live_virtual_machine_migration/24981201CC BY 4.0info:eu-repo/semantics/openAccessoai:figshare.com:article/249812012023-05-09T03:00:00Z |
| spellingShingle | A machine learning-based optimization approach for pre-copy live virtual machine migration Raseena M. Haris (17773470) Information and computing sciences Distributed computing and systems software Machine learning Cloud computing Live virtual machine migration Pre-copy Dirty page rate Machine learning Feature selection |
| status_str | publishedVersion |
| title | A machine learning-based optimization approach for pre-copy live virtual machine migration |
| title_full | A machine learning-based optimization approach for pre-copy live virtual machine migration |
| title_fullStr | A machine learning-based optimization approach for pre-copy live virtual machine migration |
| title_full_unstemmed | A machine learning-based optimization approach for pre-copy live virtual machine migration |
| title_short | A machine learning-based optimization approach for pre-copy live virtual machine migration |
| title_sort | A machine learning-based optimization approach for pre-copy live virtual machine migration |
| topic | Information and computing sciences Distributed computing and systems software Machine learning Cloud computing Live virtual machine migration Pre-copy Dirty page rate Machine learning Feature selection |