Queuing Model with Customer Class Movement across Server Groups for Analyzing Virtual Machine Migration in Cloud Computing
The advancement of cloud computing technologies has positioned virtual machine (VM) migration as a critical area of research, essential for optimizing resource management, bolstering fault tolerance, and ensuring uninterrupted service delivery. This paper offers an exhaustive analysis of VM migratio...
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| Format: | Article |
| Language: | English |
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MDPI AG
2024-02-01
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| Series: | Mathematics |
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| Online Access: | https://www.mdpi.com/2227-7390/12/3/468 |
| _version_ | 1797318464566722560 |
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| author | Anna Kushchazli Anastasia Safargalieva Irina Kochetkova Andrey Gorshenin |
| author_facet | Anna Kushchazli Anastasia Safargalieva Irina Kochetkova Andrey Gorshenin |
| author_sort | Anna Kushchazli |
| collection | DOAJ |
| description | The advancement of cloud computing technologies has positioned virtual machine (VM) migration as a critical area of research, essential for optimizing resource management, bolstering fault tolerance, and ensuring uninterrupted service delivery. This paper offers an exhaustive analysis of VM migration processes within cloud infrastructures, examining various migration types, server load assessment methods, VM selection strategies, ideal migration timing, and target server determination criteria. We introduce a queuing theory-based model to scrutinize VM migration dynamics between servers in a cloud environment. By reinterpreting resource-centric migration mechanisms into a task-processing paradigm, we accommodate the stochastic nature of resource demands, characterized by random task arrivals and variable processing times. The model is specifically tailored to scenarios with two servers and three VMs. Through numerical examples, we elucidate several performance metrics: task blocking probability, average tasks processed by VMs, and average tasks managed by servers. Additionally, we examine the influence of task arrival rates and average task duration on these performance measures. |
| first_indexed | 2024-03-08T03:52:46Z |
| format | Article |
| id | doaj.art-77f999ebd8504fdca1aac0492119720c |
| institution | Directory Open Access Journal |
| issn | 2227-7390 |
| language | English |
| last_indexed | 2024-03-08T03:52:46Z |
| publishDate | 2024-02-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Mathematics |
| spelling | doaj.art-77f999ebd8504fdca1aac0492119720c2024-02-09T15:18:27ZengMDPI AGMathematics2227-73902024-02-0112346810.3390/math12030468Queuing Model with Customer Class Movement across Server Groups for Analyzing Virtual Machine Migration in Cloud ComputingAnna Kushchazli0Anastasia Safargalieva1Irina Kochetkova2Andrey Gorshenin3Institute of Computer Science and Telecommunications, RUDN University, 6 Miklukho-Maklaya St., 117198 Moscow, RussiaInstitute of Computer Science and Telecommunications, RUDN University, 6 Miklukho-Maklaya St., 117198 Moscow, RussiaInstitute of Computer Science and Telecommunications, RUDN University, 6 Miklukho-Maklaya St., 117198 Moscow, RussiaFederal Research Center “Computer Science and Control” of the Russian Academy of Sciences, 44-2 Vavilova St., 119333 Moscow, RussiaThe advancement of cloud computing technologies has positioned virtual machine (VM) migration as a critical area of research, essential for optimizing resource management, bolstering fault tolerance, and ensuring uninterrupted service delivery. This paper offers an exhaustive analysis of VM migration processes within cloud infrastructures, examining various migration types, server load assessment methods, VM selection strategies, ideal migration timing, and target server determination criteria. We introduce a queuing theory-based model to scrutinize VM migration dynamics between servers in a cloud environment. By reinterpreting resource-centric migration mechanisms into a task-processing paradigm, we accommodate the stochastic nature of resource demands, characterized by random task arrivals and variable processing times. The model is specifically tailored to scenarios with two servers and three VMs. Through numerical examples, we elucidate several performance metrics: task blocking probability, average tasks processed by VMs, and average tasks managed by servers. Additionally, we examine the influence of task arrival rates and average task duration on these performance measures.https://www.mdpi.com/2227-7390/12/3/468cloud computingvirtual machine migrationoverloaded serverqueuing systemcontinuous-time Markov chainblocking probability |
| spellingShingle | Anna Kushchazli Anastasia Safargalieva Irina Kochetkova Andrey Gorshenin Queuing Model with Customer Class Movement across Server Groups for Analyzing Virtual Machine Migration in Cloud Computing Mathematics cloud computing virtual machine migration overloaded server queuing system continuous-time Markov chain blocking probability |
| title | Queuing Model with Customer Class Movement across Server Groups for Analyzing Virtual Machine Migration in Cloud Computing |
| title_full | Queuing Model with Customer Class Movement across Server Groups for Analyzing Virtual Machine Migration in Cloud Computing |
| title_fullStr | Queuing Model with Customer Class Movement across Server Groups for Analyzing Virtual Machine Migration in Cloud Computing |
| title_full_unstemmed | Queuing Model with Customer Class Movement across Server Groups for Analyzing Virtual Machine Migration in Cloud Computing |
| title_short | Queuing Model with Customer Class Movement across Server Groups for Analyzing Virtual Machine Migration in Cloud Computing |
| title_sort | queuing model with customer class movement across server groups for analyzing virtual machine migration in cloud computing |
| topic | cloud computing virtual machine migration overloaded server queuing system continuous-time Markov chain blocking probability |
| url | https://www.mdpi.com/2227-7390/12/3/468 |
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