Optimizing pre-copy live virtual machine migration in cloud computing using machine learning-based prediction model
<p dir="ltr">One of the preconditions for efficient cloud computing services is the continuous availability of services to clients. However, there are various reasons for temporary service unavailability due to routine maintenance, load balancing, cyber-attacks, power management, fau...
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| مؤلفون آخرون: | , , |
| منشور في: |
2024
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| _version_ | 1864513541497683968 |
|---|---|
| author | Raseena M. Haris (17773470) |
| author2 | Mahmoud Barhamgi (12618205) Armstrong Nhlabatsi (17773473) Khaled M. Khan (16888788) |
| author2_role | author author author |
| author_facet | Raseena M. Haris (17773470) Mahmoud Barhamgi (12618205) Armstrong Nhlabatsi (17773473) Khaled M. Khan (16888788) |
| author_role | author |
| dc.creator.none.fl_str_mv | Raseena M. Haris (17773470) Mahmoud Barhamgi (12618205) Armstrong Nhlabatsi (17773473) Khaled M. Khan (16888788) |
| dc.date.none.fl_str_mv | 2024-07-08T03:00:00Z |
| dc.identifier.none.fl_str_mv | 10.1007/s00607-024-01318-6 |
| dc.relation.none.fl_str_mv | https://figshare.com/articles/journal_contribution/Optimizing_pre-copy_live_virtual_machine_migration_in_cloud_computing_using_machine_learning-based_prediction_model/29899748 |
| 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 Machine learning Pre-copy memory migration Dirty pages Downtime prediction QEMU–KVM technology |
| dc.title.none.fl_str_mv | Optimizing pre-copy live virtual machine migration in cloud computing using machine learning-based prediction model |
| dc.type.none.fl_str_mv | Text Journal contribution info:eu-repo/semantics/publishedVersion text contribution to journal |
| description | <p dir="ltr">One of the preconditions for efficient cloud computing services is the continuous availability of services to clients. However, there are various reasons for temporary service unavailability due to routine maintenance, load balancing, cyber-attacks, power management, fault tolerance, emergency incident response, and resource usage. Live Virtual Machine Migration (LVM) is an option to address service unavailability by moving virtual machines between hosts without disrupting running services. Pre-copy memory migration is a common LVM approach used in cloud systems, but it faces challenges due to the high rate of frequently updated memory pages known as dirty pages. Transferring these dirty pages during pre-copy migration prolongs the overall migration time. If there are large numbers of remaining memory pages after a predefined iteration of page transfer, the stop-and-copy phase is initiated, which significantly increases downtime and negatively impacts service availability. To mitigate this issue, we introduce a prediction-based approach that optimizes the migration process by dynamically halting the iteration phase when the predicted downtime falls below a predefined threshold. Our proposed machine learning method was rigorously evaluated through experiments conducted on a dedicated testbed using KVM/QEMU technology, involving different VM sizes and memory-intensive workloads. A comparative analysis against proposed pre-copy methods and default migration approach reveals a remarkable improvement, with an average 64.91% reduction in downtime for different RAM configurations in high-write-intensive workloads, along with an average reduction in total migration time of approximately 85.81%. These findings underscore the practical advantages of our method in reducing service disruptions during live virtual machine migration in cloud systems.</p><h2>Other Information</h2><p dir="ltr">Published in: 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/s00607-024-01318-6" target="_blank">https://dx.doi.org/10.1007/s00607-024-01318-6</a></p> |
| eu_rights_str_mv | openAccess |
| id | Manara2_211f81e28fd8d80165a5d71836c260a5 |
| identifier_str_mv | 10.1007/s00607-024-01318-6 |
| network_acronym_str | Manara2 |
| network_name_str | Manara2 |
| oai_identifier_str | oai:figshare.com:article/29899748 |
| publishDate | 2024 |
| repository.mail.fl_str_mv | |
| repository.name.fl_str_mv | |
| repository_id_str | |
| rights_invalid_str_mv | CC BY 4.0 |
| spelling | Optimizing pre-copy live virtual machine migration in cloud computing using machine learning-based prediction modelRaseena M. Haris (17773470)Mahmoud Barhamgi (12618205)Armstrong Nhlabatsi (17773473)Khaled M. Khan (16888788)Information and computing sciencesDistributed computing and systems softwareMachine learningCloud computingLive virtual machine migrationMachine learningPre-copy memory migrationDirty pagesDowntime predictionQEMU–KVM technology<p dir="ltr">One of the preconditions for efficient cloud computing services is the continuous availability of services to clients. However, there are various reasons for temporary service unavailability due to routine maintenance, load balancing, cyber-attacks, power management, fault tolerance, emergency incident response, and resource usage. Live Virtual Machine Migration (LVM) is an option to address service unavailability by moving virtual machines between hosts without disrupting running services. Pre-copy memory migration is a common LVM approach used in cloud systems, but it faces challenges due to the high rate of frequently updated memory pages known as dirty pages. Transferring these dirty pages during pre-copy migration prolongs the overall migration time. If there are large numbers of remaining memory pages after a predefined iteration of page transfer, the stop-and-copy phase is initiated, which significantly increases downtime and negatively impacts service availability. To mitigate this issue, we introduce a prediction-based approach that optimizes the migration process by dynamically halting the iteration phase when the predicted downtime falls below a predefined threshold. Our proposed machine learning method was rigorously evaluated through experiments conducted on a dedicated testbed using KVM/QEMU technology, involving different VM sizes and memory-intensive workloads. A comparative analysis against proposed pre-copy methods and default migration approach reveals a remarkable improvement, with an average 64.91% reduction in downtime for different RAM configurations in high-write-intensive workloads, along with an average reduction in total migration time of approximately 85.81%. These findings underscore the practical advantages of our method in reducing service disruptions during live virtual machine migration in cloud systems.</p><h2>Other Information</h2><p dir="ltr">Published in: 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/s00607-024-01318-6" target="_blank">https://dx.doi.org/10.1007/s00607-024-01318-6</a></p>2024-07-08T03:00:00ZTextJournal contributioninfo:eu-repo/semantics/publishedVersiontextcontribution to journal10.1007/s00607-024-01318-6https://figshare.com/articles/journal_contribution/Optimizing_pre-copy_live_virtual_machine_migration_in_cloud_computing_using_machine_learning-based_prediction_model/29899748CC BY 4.0info:eu-repo/semantics/openAccessoai:figshare.com:article/298997482024-07-08T03:00:00Z |
| spellingShingle | Optimizing pre-copy live virtual machine migration in cloud computing using machine learning-based prediction model Raseena M. Haris (17773470) Information and computing sciences Distributed computing and systems software Machine learning Cloud computing Live virtual machine migration Machine learning Pre-copy memory migration Dirty pages Downtime prediction QEMU–KVM technology |
| status_str | publishedVersion |
| title | Optimizing pre-copy live virtual machine migration in cloud computing using machine learning-based prediction model |
| title_full | Optimizing pre-copy live virtual machine migration in cloud computing using machine learning-based prediction model |
| title_fullStr | Optimizing pre-copy live virtual machine migration in cloud computing using machine learning-based prediction model |
| title_full_unstemmed | Optimizing pre-copy live virtual machine migration in cloud computing using machine learning-based prediction model |
| title_short | Optimizing pre-copy live virtual machine migration in cloud computing using machine learning-based prediction model |
| title_sort | Optimizing pre-copy live virtual machine migration in cloud computing using machine learning-based prediction model |
| topic | Information and computing sciences Distributed computing and systems software Machine learning Cloud computing Live virtual machine migration Machine learning Pre-copy memory migration Dirty pages Downtime prediction QEMU–KVM technology |