HERCULE: High-Efficiency Resource Coordination Using Kubernetes and Machine Learning in Edge Computing for Improved QoS and QoE
Edge computing (EC) environments are increasingly essential in ensuring low latency and high throughput for modern applications and in smart cities. Scheduling applications in EC environments should be designed to address challenges such as uneven workload distribution, high latency, and frequent re...
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| Format: | Article |
| Language: | English |
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IEEE
2025-01-01
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| Series: | IEEE Access |
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| Online Access: | https://ieeexplore.ieee.org/document/10972108/ |
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| author | Garrik Brel Jagho Mdemaya Miguel Landry Foko Sindjoung Milliam Maxime Zekeng Ndadji Mthulisi Velempini |
| author_facet | Garrik Brel Jagho Mdemaya Miguel Landry Foko Sindjoung Milliam Maxime Zekeng Ndadji Mthulisi Velempini |
| author_sort | Garrik Brel Jagho Mdemaya |
| collection | DOAJ |
| description | Edge computing (EC) environments are increasingly essential in ensuring low latency and high throughput for modern applications and in smart cities. Scheduling applications in EC environments should be designed to address challenges such as uneven workload distribution, high latency, and frequent request retransmissions, which are not well addressed by current state-of-the-art solutions. In this work, we propose a smart Kubernetes scheduling solution that embeds a machine learning model into the Kube-scheduler for more effective application deployment. By analyzing application keywords and edge server metrics, such as geographic location, workload, and keyword-based request frequency, the proposed solution dynamically selects the optimal edge server for application deployment. The simulation results depict substantial improvements, including reduced latency, better workload equilibrium, increased achievable throughput, and minimized retransmissions between servers. Compared to existing approaches, the proposed model provides a more robust solution to the complex and dynamic requirements of edge computing environments. |
| format | Article |
| id | doaj-art-81289c2d72914e01ae8632f28c12e92a |
| institution | DOAJ |
| issn | 2169-3536 |
| language | English |
| publishDate | 2025-01-01 |
| publisher | IEEE |
| record_format | Article |
| series | IEEE Access |
| spelling | doaj-art-81289c2d72914e01ae8632f28c12e92a2025-08-20T03:14:12ZengIEEEIEEE Access2169-35362025-01-0113721537216810.1109/ACCESS.2025.356316110972108HERCULE: High-Efficiency Resource Coordination Using Kubernetes and Machine Learning in Edge Computing for Improved QoS and QoEGarrik Brel Jagho Mdemaya0https://orcid.org/0000-0003-2399-8371Miguel Landry Foko Sindjoung1https://orcid.org/0000-0002-5862-8453Milliam Maxime Zekeng Ndadji2https://orcid.org/0000-0002-0417-5591Mthulisi Velempini3https://orcid.org/0000-0003-1619-7429Department of Computer Science, University of Limpopo, Mankweng, South AfricaDepartment of Computer Science, University of Limpopo, Mankweng, South AfricaDepartment of Mathematics and Computer Science, Faculty of Science, University of Dschang, Dschang, CameroonDepartment of Computer Science, University of Limpopo, Mankweng, South AfricaEdge computing (EC) environments are increasingly essential in ensuring low latency and high throughput for modern applications and in smart cities. Scheduling applications in EC environments should be designed to address challenges such as uneven workload distribution, high latency, and frequent request retransmissions, which are not well addressed by current state-of-the-art solutions. In this work, we propose a smart Kubernetes scheduling solution that embeds a machine learning model into the Kube-scheduler for more effective application deployment. By analyzing application keywords and edge server metrics, such as geographic location, workload, and keyword-based request frequency, the proposed solution dynamically selects the optimal edge server for application deployment. The simulation results depict substantial improvements, including reduced latency, better workload equilibrium, increased achievable throughput, and minimized retransmissions between servers. Compared to existing approaches, the proposed model provides a more robust solution to the complex and dynamic requirements of edge computing environments.https://ieeexplore.ieee.org/document/10972108/Edge computingIoTKubernetesmachine learning |
| spellingShingle | Garrik Brel Jagho Mdemaya Miguel Landry Foko Sindjoung Milliam Maxime Zekeng Ndadji Mthulisi Velempini HERCULE: High-Efficiency Resource Coordination Using Kubernetes and Machine Learning in Edge Computing for Improved QoS and QoE IEEE Access Edge computing IoT Kubernetes machine learning |
| title | HERCULE: High-Efficiency Resource Coordination Using Kubernetes and Machine Learning in Edge Computing for Improved QoS and QoE |
| title_full | HERCULE: High-Efficiency Resource Coordination Using Kubernetes and Machine Learning in Edge Computing for Improved QoS and QoE |
| title_fullStr | HERCULE: High-Efficiency Resource Coordination Using Kubernetes and Machine Learning in Edge Computing for Improved QoS and QoE |
| title_full_unstemmed | HERCULE: High-Efficiency Resource Coordination Using Kubernetes and Machine Learning in Edge Computing for Improved QoS and QoE |
| title_short | HERCULE: High-Efficiency Resource Coordination Using Kubernetes and Machine Learning in Edge Computing for Improved QoS and QoE |
| title_sort | hercule high efficiency resource coordination using kubernetes and machine learning in edge computing for improved qos and qoe |
| topic | Edge computing IoT Kubernetes machine learning |
| url | https://ieeexplore.ieee.org/document/10972108/ |
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