Cargando…
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...
Autores principales: | , |
---|---|
Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
MDPI
2022
|
Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8950010/ https://www.ncbi.nlm.nih.gov/pubmed/35336575 http://dx.doi.org/10.3390/s22062404 |
_version_ | 1784675040899366912 |
---|---|
author | Yang, Jui-Pin Su, Hui-Kai |
author_facet | Yang, Jui-Pin Su, Hui-Kai |
author_sort | Yang, Jui-Pin |
collection | PubMed |
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. |
format | Online Article Text |
id | pubmed-8950010 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2022 |
publisher | MDPI |
record_format | MEDLINE/PubMed |
spelling | pubmed-89500102022-03-26 Integrated Resource Management for Fog Networks Yang, Jui-Pin Su, Hui-Kai Sensors (Basel) Communication 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. MDPI 2022-03-21 /pmc/articles/PMC8950010/ /pubmed/35336575 http://dx.doi.org/10.3390/s22062404 Text en © 2022 by the authors. https://creativecommons.org/licenses/by/4.0/Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/). |
spellingShingle | Communication Yang, Jui-Pin Su, Hui-Kai Integrated Resource Management for Fog Networks |
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 | Communication |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8950010/ https://www.ncbi.nlm.nih.gov/pubmed/35336575 http://dx.doi.org/10.3390/s22062404 |
work_keys_str_mv | AT yangjuipin integratedresourcemanagementforfognetworks AT suhuikai integratedresourcemanagementforfognetworks |