Simulated annealing based optimal controller placement in software defined networks with capacity constraint and failure awareness

Aravind, P.; Varma, Girish; Reddy, P. Jayarama

doi:10.1016/j.jksuci.2021.04.012

Cited by 11 publications

(9 citation statements)

References 24 publications

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“…The optimized controller placement solution for the CPP by considering the capacity of controllers was introduced by Ref. [31]. The load balancing method for mitigating the issues during controller placement by considering delay and controller stability was introduced by Ref.…”

Section: Problem Statementmentioning

confidence: 99%

“…Comparison of the proposed MC-SDN method with several existing methods such as Simulated Annealing Failure Foresight Capacitated Controller Placement Problem (SA-FFCCPP) [31] and Garter Snake Optimization Capacitated Controller Placement Problem (GSOCCPP) [33] is performed in this subsection to evaluate their performance. The evaluation of these methods is performed by considering several performance metrics such as propagation delay, average round-trip time (RTT), matrix of time Session (TS), average delay, reliability, and throughput, respectively.…”

Section: Comparative Analysismentioning

confidence: 99%

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Multi-Controllers Placement Optimization in SDN by the Hybrid HSA-PSO Algorithm

2022

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Software-Defined Networking (SDN) is a developing architecture that provides scalability, flexibility, and efficient network management. However, optimal controller placement faces many problems, which affect the performance of the overall network. To resolve the Multi-controller SDN (MC-SDN) that is deployed in the SDN environment, we propose an approach that uses a hybrid metaheuristic algorithm that improves network performance. Initially, the proposed SDN network is constructed based on graph theory, which improves the connectivity and flexibility between switches and controllers. After that, the controller selection is performed by selecting an optimal controller from multiple controllers based on controller features using the firefly optimization algorithm (FA), which improves the network performance. Finally, multi-controller placement is performed to reduce the communication latency between the switch to controllers. Here, multiple controllers are placed by considering location and distance using a hybrid metaheuristic algorithm, which includes a harmonic search algorithm and particle swarm optimization algorithm (HSA-PSO), in which the PSO algorithm is proposed to automatically update the harmonic search parameters. The simulation of multi-controller placement is carried out by the CloudsimSDN network simulator, and the simulation results demonstrate the proposed advantages in terms of propagation latency, Round Trip Time (RTT), matrix of Time Session (TS), delay, reliability, and throughput.

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Section: Problem Statementmentioning

confidence: 99%

Section: Comparative Analysismentioning

confidence: 99%

Multi-Controllers Placement Optimization in SDN by the Hybrid HSA-PSO Algorithm

2022

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“…P. Aravind et al 17 have used a heuristic method based on SA to overcome the problem of allocating switches within a short time for various networks. Their model was able to solve the problem within a reasonable amount of time for varied network sizes.…”

Section: Literature Reviewmentioning

confidence: 99%

Performance Analysis of Simulated Annealing and Genetic Algorithm on systems of linear equations

et al. 2021

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Problem solving and modelling in traditional substitution methods at large scale for systems using sets of simultaneous equations is time consuming. For such large scale global-optimization problem, Simulated Annealing (SA) algorithm and Genetic Algorithm (GA) as meta-heuristics for random search technique perform faster. Therefore, this study applies the SA to solve the problem of linear equations and evaluates its performances against Genetic Algorithms (GAs), a population-based search meta-heuristic, which are widely used in Travelling Salesman problems (TSP), Noise reduction and many more. This paper presents comparison between performances of the SA and GA for solving real time scientific problems. The significance of this paper is to solve the certain real time systems with a set of simultaneous linear equations containing different unknown variable samples those were simulated in Matlab using two algorithms-SA and GA. In all of the experiments, the generated random initial solution sets and the random population of solution sets were used in the SA and GA respectively. The comparison and performances of the SA and GA were evaluated for the optimization to take place for providing sets of solutions on certain systems. The SA algorithm is superior to GA on the basis of experimentation done on the sets of simultaneous equations, with a lower fitness function evaluation count in MATLAB simulation. Since, complex non-linear systems of equations have not been the primary focus of this research, in future, performances of SA and GA using such equations will be addressed. Even though GA maintained a relatively lower number of average generations than SA, SA still managed to outperform GA with a reasonably lower fitness function evaluation count. Although SA sometimes converges slowly, still it is efficient for solving problems of simultaneous equations in this case. In terms of computational complexity, SA was far more superior to GAs.

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“…In the recent era, complex wireless sensor networks (WSNs) have exhibited enhanced features that aid in improving network resource management (NRM) effectively. 1 These features, including connections, coupling, and dynamics, are utilized effectively in mobile communication and the Internet of Things (IoT). 2 However, adopting these features makes the NRM and optimization complex, creating several network constraints and making it hard to cope with the recent • to introduce a novel strategy (sailfish optimization-fuzzy-C-means [SO-FCM]) for node clustering and controller placement (CP) in the software-defined wireless sensor networks (SDWSN); • to propose a SO technique for the optimal placement of controllers in the SDWSN networks.…”

Section: Introductionmentioning

confidence: 99%

“…In the recent era, complex wireless sensor networks (WSNs) have exhibited enhanced features that aid in improving network resource management (NRM) effectively 1 . These features, including connections, coupling, and dynamics, are utilized effectively in mobile communication and the Internet of Things (IoT) 2 .…”

Section: Introductionmentioning

confidence: 99%

SO‐CPP: Sailfish optimization‐based controller placement in IoT‐enabled software‐defined wireless sensor networks

Kumari Rajoriya,

Gupta

2024

Int J Communication

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SummaryOne of the expanding network topologies that is frequently utilized to improve network development by successfully separating the control plane and data plane is software‐defined networking (SDN). In order to function inside complex sensor networks, the SDWSN system frequently relies on centralized controller logic that pulls global network information. In wireless sensor networks (WSNs), using several SDN controllers is known as a promising strategy due to reliability and performance considerations. However, using numerous controllers increases the synchronization overhead between the controllers. Consequently, it is a difficult research challenge to discover the best placement of SDN controllers to enhance the performance of a WSN, subject to the maximum number of controllers calculated based on the synchronization overhead. This research introduces a novel technique to overcome the controller placement problem (CPP) by optimizing multi‐constraints within the sensor networks. For selecting the optimal controllers and placing them in an optimal location, a novel sailfish optimization (SO) strategy is introduced that can enhance the search space and maintain optimal global values throughout the iteration. Then, node clustering is performed using the fuzzy‐C‐means (FCM) clustering technique, which can reduce energy consumption and path delay within the network. The overall latency obtained by the proposed method is about 0.51 and 0.56 ms, and a total run time of 4 ms for both single sink and multi‐sink, respectively. The proposed method is implemented in the MATLAB platform, and different performance metrics are analyzed and compared with existing techniques.

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Simulated annealing based optimal controller placement in software defined networks with capacity constraint and failure awareness

Cited by 11 publications

References 24 publications

Multi-Controllers Placement Optimization in SDN by the Hybrid HSA-PSO Algorithm

Multi-Controllers Placement Optimization in SDN by the Hybrid HSA-PSO Algorithm

Performance Analysis of Simulated Annealing and Genetic Algorithm on systems of linear equations

SO‐CPP: Sailfish optimization‐based controller placement in IoT‐enabled software‐defined wireless sensor networks

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