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SOSW : scalable and optimal nearsighted location selection for fog node deployment and routing in SDN-based wireless networks for IoT systems
Annals of Telecommunications ( IF 1.9 ) Pub Date : 2021-04-22 , DOI: 10.1007/s12243-021-00845-z
Muhammad Ibrar , Lei Wang , Gabriel-Miro Muntean , Nadir Shah , Aamir Akbar , Khalid Ibrahim Qureshi

In a fog computing (FC) architecture, cloud services migrate towards the network edge and operate via edge devices such as access points (AP), routers, and switches. These devices become part of a virtualization infrastructure and are referred to as “fog nodes.” Recently, software-defined networking (SDN) has been used in FC to improve its control and manageability. The current SDN-based FC literature has overlooked two issues: (a) fog nodes’ deployment at optimal locations and (b) SDN best path computation for data flows based on constraints (i.e., end-to-end delay and link utilization). To solve these optimization problems, this paper suggests a novel approach, called scalable and optimal near-sighted location selection for fog node deployment and routing in SDN-based wireless networks for IoT systems (SOSW). First, the SOSW model uses singular-value decomposition (SVD) and QR factorization with column pivoting linear algebra methods on the traffic matrix of the network to compute the optimal locations for fog nodes, and second, it introduces a new heuristic-based traffic engineering algorithm, called the constraint-based shortest path algorithm (CSPA), which uses ant colony optimization (ACO) to optimize the path computation process for task offloading. The results show that our proposed approach significantly reduces average latency and energy consumption in comparison with existing approaches.



中文翻译:

SOSW:可扩展和最佳的近视位置选择,用于物联网系统中基于SDN的无线网络中的雾节点部署和路由

在雾计算(FC)架构中,云服务向网络边缘迁移,并通过诸如接入点(AP),路由器和交换机之类的边缘设备运行。这些设备成为虚拟化基础架构的一部分,被称为“雾节点”。最近,FC中使用了软件定义的网络(SDN)来改善其控制和可管理性。当前基于SDN的FC文献忽略了两个问题:(a)雾节点部署在最佳位置,以及(b)SDN基于约束(即端到端延迟和链路利用率)的数据流最佳路径计算。为了解决这些优化问题,本文提出了一种新颖的方法,称为可扩展且最佳的近视位置选择,用于物联网系统 SDN)的基于SDN的无线网络中的雾节点部署和路由。首先,SOSW模型在网络流量矩阵上使用奇异值分解(SVD)和QR分解以及列枢轴线性代数方法来计算雾节点的最佳位置,其次,引入了一种新的基于启发式的流量工程该算法称为基于约束的最短路径算法(CSPA),该算法使用蚁群优化(ACO)来优化任务卸载的路径计算过程。结果表明,与现有方法相比,我们提出的方法显着减少了平均延迟和能耗。

更新日期:2021-04-22
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