An edge server placement based on graph clustering in mobile edge computing
Abstract With the exponential growth of mobile devices and data traffic, mobile edge computing has become a promising technology, and the placement of edge servers plays a key role in providing efficient and low-latency services. In this paper, we investigate the issue of edge server placement and u...
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| Format: | Article |
| Language: | English |
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Nature Portfolio
2024-12-01
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| Series: | Scientific Reports |
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| Online Access: | https://doi.org/10.1038/s41598-024-81684-5 |
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| author | Shanshan Zhang Jiong Yu Mingjian Hu |
| author_facet | Shanshan Zhang Jiong Yu Mingjian Hu |
| author_sort | Shanshan Zhang |
| collection | DOAJ |
| description | Abstract With the exponential growth of mobile devices and data traffic, mobile edge computing has become a promising technology, and the placement of edge servers plays a key role in providing efficient and low-latency services. In this paper, we investigate the issue of edge server placement and user allocation to reduce transmission delay between base stations and servers, and balance the workload of individual servers. To this end, we propose a graph clustering-based edge server placement model by fully considering the constraints such as the distance, coverage area and number of channels of base stations. The model mainly consists of a two-layer graph convolutional network (GCN) component and a differentiable version of K-means clustering component, which transforms the server placement problem into an end-to-end learning optimization problem on a graph. It trains the GCN network to achieve the best clustering results with the expectation of average delay and load balancing as the loss function to obtain the edge server placement and user assignment scheme. We conducted experiments based on the Shanghai Telecom dataset, and the results show the effectiveness of our approach in both latency reduction and load balancing. |
| format | Article |
| id | doaj-art-1f8d6e72af5f4d8db0619c7b6009e9b6 |
| institution | Kabale University |
| issn | 2045-2322 |
| language | English |
| publishDate | 2024-12-01 |
| publisher | Nature Portfolio |
| record_format | Article |
| series | Scientific Reports |
| spelling | doaj-art-1f8d6e72af5f4d8db0619c7b6009e9b62024-12-08T12:29:22ZengNature PortfolioScientific Reports2045-23222024-12-0114111910.1038/s41598-024-81684-5An edge server placement based on graph clustering in mobile edge computingShanshan Zhang0Jiong Yu1Mingjian Hu2School of Computer Science and Technology, Xinjiang UniversitySchool of Computer Science and Technology, Xinjiang UniversityXinjiang Petroleum Engineering Co., LtdAbstract With the exponential growth of mobile devices and data traffic, mobile edge computing has become a promising technology, and the placement of edge servers plays a key role in providing efficient and low-latency services. In this paper, we investigate the issue of edge server placement and user allocation to reduce transmission delay between base stations and servers, and balance the workload of individual servers. To this end, we propose a graph clustering-based edge server placement model by fully considering the constraints such as the distance, coverage area and number of channels of base stations. The model mainly consists of a two-layer graph convolutional network (GCN) component and a differentiable version of K-means clustering component, which transforms the server placement problem into an end-to-end learning optimization problem on a graph. It trains the GCN network to achieve the best clustering results with the expectation of average delay and load balancing as the loss function to obtain the edge server placement and user assignment scheme. We conducted experiments based on the Shanghai Telecom dataset, and the results show the effectiveness of our approach in both latency reduction and load balancing.https://doi.org/10.1038/s41598-024-81684-5Edge server placementMobile edge computingGraph clusteringGraph convolutional network |
| spellingShingle | Shanshan Zhang Jiong Yu Mingjian Hu An edge server placement based on graph clustering in mobile edge computing Scientific Reports Edge server placement Mobile edge computing Graph clustering Graph convolutional network |
| title | An edge server placement based on graph clustering in mobile edge computing |
| title_full | An edge server placement based on graph clustering in mobile edge computing |
| title_fullStr | An edge server placement based on graph clustering in mobile edge computing |
| title_full_unstemmed | An edge server placement based on graph clustering in mobile edge computing |
| title_short | An edge server placement based on graph clustering in mobile edge computing |
| title_sort | edge server placement based on graph clustering in mobile edge computing |
| topic | Edge server placement Mobile edge computing Graph clustering Graph convolutional network |
| url | https://doi.org/10.1038/s41598-024-81684-5 |
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