Integrated Resource Management for Fog Networks
In this paper, we consider integrated resource management for fog networks inclusive of intelligent energy perception, service level agreement (SLA) planning and replication-based hotspot offload (RHO). In the beginning, we propose an intelligent energy perception scheme which dynamically classifies...
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| Format: | Article |
| Language: | English |
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MDPI AG
2022-03-01
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| Series: | Sensors |
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| Online Access: | https://www.mdpi.com/1424-8220/22/6/2404 |
| _version_ | 1797442254497906688 |
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| author | Jui-Pin Yang Hui-Kai Su |
| author_facet | Jui-Pin Yang Hui-Kai Su |
| author_sort | Jui-Pin Yang |
| collection | DOAJ |
| description | In this paper, we consider integrated resource management for fog networks inclusive of intelligent energy perception, service level agreement (SLA) planning and replication-based hotspot offload (RHO). In the beginning, we propose an intelligent energy perception scheme which dynamically classifies the fog nodes into a hot set, a warm set or a cold set, based on their load conditions. The fog nodes in the hot set are responsible for a quality of service (QoS) guarantee and the fog nodes in the cold set are maintained at a low-energy state to save energy consumption. Moreover, the fog nodes in the warm set are used to balance the QoS guarantee and energy consumption. Secondly, we propose an SLA mapping scheme which effectively identifies the SLA elements with the same semantics. Finally, we propose a replication-based load-balancing scheme, namely RHO. The RHO can leverage the skewed access pattern caused by the hotspot services. In addition, it greatly reduces communication overheads because the load conditions are updated only when the load variations exceed a specific threshold. Finally, we use computer simulations to compare the performance of the RHO with other schemes under a variety of load conditions. In a word, we propose a comprehensive and feasible solution that contributes to the integrated resource management of fog networks. |
| first_indexed | 2024-03-09T12:39:10Z |
| format | Article |
| id | doaj.art-b640a613ba0c4ff9973a2ba056f4731b |
| institution | Directory Open Access Journal |
| issn | 1424-8220 |
| language | English |
| last_indexed | 2024-03-09T12:39:10Z |
| publishDate | 2022-03-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Sensors |
| spelling | doaj.art-b640a613ba0c4ff9973a2ba056f4731b2023-11-30T22:20:51ZengMDPI AGSensors1424-82202022-03-01226240410.3390/s22062404Integrated Resource Management for Fog NetworksJui-Pin Yang0Hui-Kai Su1Department of Information Technology and Communication, Shih-Chien University, Kaohsiung 845, TaiwanDepartment of Electrical Engineering, National Formosa University, Yunlin 632, TaiwanIn this paper, we consider integrated resource management for fog networks inclusive of intelligent energy perception, service level agreement (SLA) planning and replication-based hotspot offload (RHO). In the beginning, we propose an intelligent energy perception scheme which dynamically classifies the fog nodes into a hot set, a warm set or a cold set, based on their load conditions. The fog nodes in the hot set are responsible for a quality of service (QoS) guarantee and the fog nodes in the cold set are maintained at a low-energy state to save energy consumption. Moreover, the fog nodes in the warm set are used to balance the QoS guarantee and energy consumption. Secondly, we propose an SLA mapping scheme which effectively identifies the SLA elements with the same semantics. Finally, we propose a replication-based load-balancing scheme, namely RHO. The RHO can leverage the skewed access pattern caused by the hotspot services. In addition, it greatly reduces communication overheads because the load conditions are updated only when the load variations exceed a specific threshold. Finally, we use computer simulations to compare the performance of the RHO with other schemes under a variety of load conditions. In a word, we propose a comprehensive and feasible solution that contributes to the integrated resource management of fog networks.https://www.mdpi.com/1424-8220/22/6/2404resource managementfog networkenergy perceptionservice level agreementload balancinghotspot offload |
| spellingShingle | Jui-Pin Yang Hui-Kai Su Integrated Resource Management for Fog Networks Sensors resource management fog network energy perception service level agreement load balancing hotspot offload |
| title | Integrated Resource Management for Fog Networks |
| title_full | Integrated Resource Management for Fog Networks |
| title_fullStr | Integrated Resource Management for Fog Networks |
| title_full_unstemmed | Integrated Resource Management for Fog Networks |
| title_short | Integrated Resource Management for Fog Networks |
| title_sort | integrated resource management for fog networks |
| topic | resource management fog network energy perception service level agreement load balancing hotspot offload |
| url | https://www.mdpi.com/1424-8220/22/6/2404 |
| work_keys_str_mv | AT juipinyang integratedresourcemanagementforfognetworks AT huikaisu integratedresourcemanagementforfognetworks |