Abstract
Software-Defined Networking (SDN) is a network paradigm introduced to overcome the inherent challenges of traditional networks. Its architecture is either deployed with a single controller or multiple controllers. While the first is not suitable for large-scale networks, the latter is confronted with a controller placement problem (CPP) in a large-scale network environment. CPP involves the challenge of deploying the optimal number of controllers within a network while meeting certain performance requirements considered conflicting in nature such as reliability, load balancing, latency, energy efficiency, and computation time. A single optimal or random placement may not be feasible in CPP and careful planning is of the essence to find an appropriate trade-off among the metrics. To achieve this, several CPP approaches have been proposed, developed, and deployed over the years, each having its unique objectives, strengths, and weaknesses. Therefore, this paper performed a comprehensive review of some of the existing approaches to identify the unique solutions offered, comprehend the different strategies and the challenges that exist as well as provide researchers with future directions aimed at improving the optimum location and allocation of controllers, in particular, for SDN application in wireless sensor network (WSN). The findings revealed several existing solutions and algorithms as well as several challenges such as the need for an efficient algorithm, attack-aware, cost-aware, and energy-aware CPP schemes while achieving a good quality of service.
Highlights
Software-Defined Networking (SDN) is a networking paradigm that emerged in recent years via several initiatives and standards to ensure network flexibility, efficient utilization, cost-effectiveness, and innovation [1]–[3]
The authors considered propagation latency, reliability, and load balancing as the important performance metrics and proposed a Spectral clustering Placement Algorithm that achieves the task of large network petitioning into different domains
In [12], Ahmadi and Khorramizade proposed a large-scale multi-objective controller placement approach using a fast and efficient adaptation of evolutionary algorithms. They utilized a heuristic algorithm known as Multi-Start Hybrid NonDominated Sorting Genetic Algorithm (MHNSGA) while considering performance metrics such as SC-latency, CClatency, load balancing, and reliability for link failures in the objective function
Summary
SDN is a networking paradigm that emerged in recent years via several initiatives and standards to ensure network flexibility, efficient utilization, cost-effectiveness, and innovation [1]–[3]. CPP is aimed at finding the optimal location of the SDN controllers in a manner that achieves various defined objectives such as latency minimization, load balancing, energy efficiency, and enhanced reliability [2], [3], [13] which are critical to SDN’s performance in large-scale networks. 2) OTHER CLUSTERED BASED ALGORITHMS When considering multiple objectives with the consideration of the optimal number of nodes within a cluster, a more efficient, or optimized approach is important In this regard, Liu et al [38] CPP strategy aimed to optimize the network average reliability. The authors considered propagation latency, reliability, and load balancing as the important performance metrics and proposed a Spectral clustering Placement Algorithm that achieves the task of large network petitioning into different domains. The approach was evaluated using real-world network topologies and the results obtained showed improved performance by reducing the maximum latency
Sign-in/Register to view highlights & summary Sign-in/Register to access full text options 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callDisclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
