A new framework for reliable control placement in software-defined networks based on multi-criteria clustering approach

Jalili, Ahmad; Keshtgari, Manijeh; Akbari, Reza

doi:10.1007/s00500-019-04070-8

Cited by 25 publications

(17 citation statements)

References 41 publications

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“…The experimental results demonstrate that the proposed work achieved better performance in fault tolerance compared to existing works. A novel controller placement framework was proposed using multi-criteria-based clustering method [23]. The main aim of this research was to reduce communication latency and end-to-end delay.…”

Section: Literature Surveymentioning

confidence: 99%

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: Literature Surveymentioning

confidence: 99%

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

2022

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“…Both constraints (15) and (16) are related to the flow variables and the assignment decision, which belongs to the same controller. The requested flow by a switch can be set by the controller j, only if it is available on the controller, j.…”

Section: Dynamic Assignment Costmentioning

confidence: 99%

“…It is important to assign user requests to different cluster controllers to balance the workload between them. When decreasing the overall response time for offloaded tasks, significant consideration should be given toward planning the required demand for traffic volume and service request control tools [15].…”

Section: Introductionmentioning

confidence: 99%

Heuristic Resource Allocation Algorithm for Controller Placement in Multi-Control 5G Based on SDN/NFV Architecture

et al. 2021

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“…To ensure consistency between the controllers in the SDN, communication between the controllers is also required. Therefore, the delay between the controllers is also a key CPP indicator [14][15][16][17][18][19][20][21][22][23][24][25][26][27][28][29]. Besides, placement cost [21], network fault tolerance [28], path failure probability [30] and the like, are common CPP indicators.…”

Section: Related Workmentioning

confidence: 99%

“…These clustering-based controller placement algorithms first divide the entire SDN into multiple subnets and then determine the location of each controller to minimize the delay between the controllers and the switches. However, more controller placement algorithms were designed based on the heuristic algorithm, for example, the controller placement algorithm based on the physical law heuristic algorithm: simulated annealing algorithm [15]; the controller placement algorithm based on the swarm intelligent behavior heuristic algorithm, which includes the PSO [18], FA [25], moth flame optimization [27], and adaptive bacterial foraging optimization [30]; the controller placement algorithm based on the human behavior heuristic algorithm, which includes TLBO [20] and VBO [29]; and the controller placement algorithm based on the evolutionary behavior heuristic algorithm, which includes non-dominated sorting genetic algorithm [17], nondominated sorting genetic algorithm II [22], and genetic algorithm [32]. The controller placement algorithm based on the heuristic algorithm usually simulates the coordinates of the controllers which need to be placed in the network into a multi-dimensional object in the heuristic algorithm, uses the location update strategy, sets the objective function, and iteratively solves the CPP.…”

Section: Related Workmentioning

confidence: 99%

Parameter Optimization Model of Heuristic Algorithms for Controller Placement Problem in Large-Scale SDN

Guan

Zhang

et al. 2020

IEEE Access

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The Controller Placement Problem (CPP) is a key technical challenge in a large-scale Software Defined Network (SDN). Low-complexity heuristic algorithm is widely used for solving the CPP. However, parameter settings of the heuristic algorithm greatly affect the result of the CPP. Therefore, we establish a Parameter Optimization Model (POM) for the heuristic algorithm applied to the CPP. The heuristic algorithm can effectively solve the CPP by using the optimized parameters obtained in POM. To verify the effectiveness of the POM, we first establish a synthetical-delay controller placement model to reduce the delay between the controllers and the switches and the delay between the controllers. Further, we select the bat algorithm, the firefly algorithm, and the varna-based optimization respectively to solve the model, and use the particle swarm optimization method to optimize the parameters of the three algorithms. Experimental results on real topologies show that compared with original algorithms and other similar algorithms, the algorithms with optimized parameters perform better. INDEX TERMS Controller placement problem, delay, heuristic algorithm, parameter optimization, particle swarm optimization, software defined network.

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A new framework for reliable control placement in software-defined networks based on multi-criteria clustering approach

Cited by 25 publications

References 41 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

Heuristic Resource Allocation Algorithm for Controller Placement in Multi-Control 5G Based on SDN/NFV Architecture

Parameter Optimization Model of Heuristic Algorithms for Controller Placement Problem in Large-Scale SDN

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