SOSW: scalable and optimal nearsighted location selection for fog node deployment and routing in SDN-based wireless networks for IoT systems

Abstract

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.

Comparing the Scalability of Communication Networks and Systems

“…As a concrete example from literature how to use the SI to compare the scalability of different solution approaches, the location selection for fog node deployment and routing in SDN-based wireless networks for IoT systems is investigated [26]. The interesting aspect of that use case is that we need to consider two different target measures, which are the average end-to-end delay as well as the energy consumption.…”

“…The fog computing architecture involves relocating services such as computing, processing, and storage from the centralized cloud to the network edge or nearby devices, where these services are deployed. [26] introduces a novel approach called ''Scalable and Optimal Near-Sighted Location Selection'' (SOSW) to address two key issues in fog computing architecture with software-defined networking (SDN). (i) Fog nodes are strategically deployed for optimal performance.…”

“…A power law prediction of the delay curve is provided in Figure 18b. [26]. In addition, the SI is computed for different approaches with respect to a linear reference system.…”

“…FIGURE 18.Example of location selection for fog node deployment and routing in SDN-based wireless networks for IoT systems. The data is taken from[26]. In addition, the SI is computed for different approaches with respect to a linear reference system.…”

DD-FoG: Intelligent Distributed Dynamic FoG Computing Framework

“…Within one fog-zone, there will be areas with different density of congestion of service consumers [20,21]. Moreover, the sizes of such clusters are also not the same.…”