Suppr超能文献

利用雾计算中的 SDN 卸载技术减少移动边缘计算中的传输延迟。

Delay reduction in MTC using SDN based offloading in Fog computing.

机构信息

Department of Computing Architecture, Faculty of Computer Engineering, University of Isfahan, Isfahan, Iran.

出版信息

PLoS One. 2023 May 30;18(5):e0286483. doi: 10.1371/journal.pone.0286483. eCollection 2023.

Abstract

Fog computing (FC) brings a Cloud close to users and improves the quality of service and delay services. In this article, the convergence of FC and Software-Defined-Networking (SDN) has been proposed to implement complicated mechanisms of resource management. SDN has suited the practical standard for FC systems. The priority and differential flow space allocation have been applied to arrange this framework for the heterogeneous request in Machine-Type-Communications. The delay-sensitive flows are assigned to a configuration of priority queues on each Fog. Due to limited resources in the Fog, a promising solution is offloading flows to other Fogs through a decision-based SDN controller. The flow-based Fog nodes have been modeled according to the queueing theory, where polling priority algorithms have been applied to service the flows and to reduce the starvation problem in a multi-queueing model. It is observed that the percentage of delay-sensitive processed flows, the network consumption, and the average service time in the proposed mechanism are improved by about 80%, 65%, and 60%, respectively, compared to traditional Cloud computing. Therefore, the delay reductions based on the types of flows and task offloading is proposed.

摘要

雾计算(FC)将云靠近用户,并提高服务质量和延迟服务。在本文中,提出了 FC 和软件定义网络(SDN)的融合,以实现资源管理的复杂机制。SDN 已经适合 FC 系统的实际标准。优先级和差分流空间分配已应用于为机器类型通信中的异构请求安排此框架。延迟敏感流被分配到每个雾中的优先级队列配置。由于雾中的资源有限,一种有前途的解决方案是通过基于决策的 SDN 控制器将流卸载到其他雾中。基于流的雾节点已根据排队论进行建模,其中已应用轮询优先级算法来服务流,并减少多队列模型中的饥饿问题。与传统云计算相比,观察到所提出的机制中延迟敏感处理流的百分比、网络消耗和平均服务时间分别提高了约 80%、65%和 60%。因此,提出了基于流类型和任务卸载的延迟减少。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/7227/10228777/f299952ee531/pone.0286483.g001.jpg

相似文献

1
Delay reduction in MTC using SDN based offloading in Fog computing.
PLoS One. 2023 May 30;18(5):e0286483. doi: 10.1371/journal.pone.0286483. eCollection 2023.
3
An Intelligent Proposed Model for Task Offloading in Fog-Cloud Collaboration Using Logistics Regression.
Comput Intell Neurosci. 2022 Jan 25;2022:3606068. doi: 10.1155/2022/3606068. eCollection 2022.
4
Online Workload Allocation via Fog-Fog-Cloud Cooperation to Reduce IoT Task Service Delay.
Sensors (Basel). 2019 Sep 4;19(18):3830. doi: 10.3390/s19183830.
6
A Multi-Classifiers Based Algorithm for Energy Efficient Tasks Offloading in Fog Computing.
Sensors (Basel). 2023 Aug 16;23(16):7209. doi: 10.3390/s23167209.
9
Dynamically Controlling Offloading Thresholds in Fog Systems.
Sensors (Basel). 2021 Apr 3;21(7):2512. doi: 10.3390/s21072512.
10
Communication and Computing Task Allocation for Energy-Efficient Fog Networks.
Sensors (Basel). 2023 Jan 15;23(2):997. doi: 10.3390/s23020997.

本文引用的文献

2
A Survey of Security in Cloud, Edge, and Fog Computing.
Sensors (Basel). 2022 Jan 25;22(3):927. doi: 10.3390/s22030927.
3
Modified firefly algorithm for workflow scheduling in cloud-edge environment.
Neural Comput Appl. 2022;34(11):9043-9068. doi: 10.1007/s00521-022-06925-y. Epub 2022 Feb 2.

文献AI研究员

20分钟写一篇综述,助力文献阅读效率提升50倍。

立即体验

用中文搜PubMed

大模型驱动的PubMed中文搜索引擎

马上搜索

文档翻译

学术文献翻译模型,支持多种主流文档格式。

立即体验