Utility-driven virtual machine allocation in edge cloud environments using a partheno-genetic algorithm
Abstract Mobile Edge Computing alleviates network congestion and reduces latency by offloading tasks to the network edge. However, fluctuating Quality of Service (QoS) and service compositions significantly challenge service reliability and utility optimization. To address these challenges, this pap...
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| Format: | Article |
| Language: | English |
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SpringerOpen
2025-03-01
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| Series: | Journal of Cloud Computing: Advances, Systems and Applications |
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| Online Access: | https://doi.org/10.1186/s13677-025-00739-8 |
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| _version_ | 1849762462172184576 |
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| author | Jie Cao Cuicui Zhang Ping Qi Kekun Hu |
| author_facet | Jie Cao Cuicui Zhang Ping Qi Kekun Hu |
| author_sort | Jie Cao |
| collection | DOAJ |
| description | Abstract Mobile Edge Computing alleviates network congestion and reduces latency by offloading tasks to the network edge. However, fluctuating Quality of Service (QoS) and service compositions significantly challenge service reliability and utility optimization. To address these challenges, this paper proposes a novel virtual machine allocation framework designed to maximize the utility of edge cloud service provisioning under QoS constraints. First, the task processing mechanism is modeled as an M/M/m queuing system, with service loss and revenue functions defined to quantify the quality and profitability of edge services. Next, the framework dynamically reallocates virtual machines across sub-service centers, based on task arrival rates and varying QoS requirements, to optimize overall service utility. Finally, we develop a partheno-genetic algorithm based on integer coding to solve the service utility maximization (SOPGA) to determine the optimal virtual machine allocation strategy. Simulation results demonstrate that the proposed virtual machine allocation algorithm improves service utility by more than 20% compared to other virtual machine allocation algorithms, significantly enhancing service utility in edge cloud environments while maintaining robust QoS guarantees. |
| format | Article |
| id | doaj-art-1f374dd32563468ebf869b30e67be44a |
| institution | DOAJ |
| issn | 2192-113X |
| language | English |
| publishDate | 2025-03-01 |
| publisher | SpringerOpen |
| record_format | Article |
| series | Journal of Cloud Computing: Advances, Systems and Applications |
| spelling | doaj-art-1f374dd32563468ebf869b30e67be44a2025-08-20T03:05:44ZengSpringerOpenJournal of Cloud Computing: Advances, Systems and Applications2192-113X2025-03-0114111510.1186/s13677-025-00739-8Utility-driven virtual machine allocation in edge cloud environments using a partheno-genetic algorithmJie Cao0Cuicui Zhang1Ping Qi2Kekun Hu3School of Mathematics and Computer Science, Tongling UniversitySchool of Software, Zhengzhou University of Light IndustrySchool of Mathematics and Computer Science, Tongling UniversityIEIT SYSTEMS Co., LtdAbstract Mobile Edge Computing alleviates network congestion and reduces latency by offloading tasks to the network edge. However, fluctuating Quality of Service (QoS) and service compositions significantly challenge service reliability and utility optimization. To address these challenges, this paper proposes a novel virtual machine allocation framework designed to maximize the utility of edge cloud service provisioning under QoS constraints. First, the task processing mechanism is modeled as an M/M/m queuing system, with service loss and revenue functions defined to quantify the quality and profitability of edge services. Next, the framework dynamically reallocates virtual machines across sub-service centers, based on task arrival rates and varying QoS requirements, to optimize overall service utility. Finally, we develop a partheno-genetic algorithm based on integer coding to solve the service utility maximization (SOPGA) to determine the optimal virtual machine allocation strategy. Simulation results demonstrate that the proposed virtual machine allocation algorithm improves service utility by more than 20% compared to other virtual machine allocation algorithms, significantly enhancing service utility in edge cloud environments while maintaining robust QoS guarantees.https://doi.org/10.1186/s13677-025-00739-8Mobile edge computingRandom task processingService lossService revenuePartheno-genetic algorithm |
| spellingShingle | Jie Cao Cuicui Zhang Ping Qi Kekun Hu Utility-driven virtual machine allocation in edge cloud environments using a partheno-genetic algorithm Journal of Cloud Computing: Advances, Systems and Applications Mobile edge computing Random task processing Service loss Service revenue Partheno-genetic algorithm |
| title | Utility-driven virtual machine allocation in edge cloud environments using a partheno-genetic algorithm |
| title_full | Utility-driven virtual machine allocation in edge cloud environments using a partheno-genetic algorithm |
| title_fullStr | Utility-driven virtual machine allocation in edge cloud environments using a partheno-genetic algorithm |
| title_full_unstemmed | Utility-driven virtual machine allocation in edge cloud environments using a partheno-genetic algorithm |
| title_short | Utility-driven virtual machine allocation in edge cloud environments using a partheno-genetic algorithm |
| title_sort | utility driven virtual machine allocation in edge cloud environments using a partheno genetic algorithm |
| topic | Mobile edge computing Random task processing Service loss Service revenue Partheno-genetic algorithm |
| url | https://doi.org/10.1186/s13677-025-00739-8 |
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