A novel controller placement algorithm based on network portioning concept and a hybrid discrete optimization algorithm for multi-controller software-defined networks

Firouz, Nasrin; Màsdàrí, Mohàmmàd; Sangar, Amin Babazadeh; Majidzadeh, Kambiz

doi:10.1007/s10586-021-03264-w

Cited by 27 publications

(16 citation statements)

References 64 publications

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“…Where g uj is the total number of users covered by UAV u j . The last objective is to minimize as much as possible the equal load distribution based on the UAV location by Equation (14). min ELD…”

Section: ) Load Distributionmentioning

confidence: 99%

“…Among existing meta-heuristics, the Manta Ray Foraging optimization algorithm is a recent Swarm Intelligence optimization algorithm developed by Zhao et al [2] in 2020. Due to its simplicity and easy implementation, it was widely applied for solving optimization problems such as electrical engineering [3], [4], image processing [5], [6], mathematics [7], geology [8], feature selection [9], system identification [10], energy [11]- [13], networking [14], PID control [15], and many others. Similar to other meta-heuristics, the MRFO algorithm suffers from premature and slow convergence and attempts to fall to local optima.…”

Section: Introductionmentioning

confidence: 99%

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A Hybrid Improved Manta Ray Foraging Optimization With Tabu Search Algorithm for Solving the UAV Placement Problem in Smart Cities

et al. 2023

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The concept of smart cities is to enhance the life quality of residents and provide efficient services by integrating advanced information and communication technologies, autonomous robots, Internet of Things (IoT) devices, etc. Unmanned Aerial Vehicles (UAVs) are a class of autonomous flying mobile robots that bring a lot of benefits to smart cities due to their mobility, accessibility, autonomy, and many other advantages. Their integration allows for accomplishing hard and complex tasks that humans or other entities are not able to complete. In most applications, multiple connected UAVs are required to build a network under which missions are completed and tasks are shared. This network can be established by finding the optimal UAV placement that meets some requirements such as user coverage, UAV connectivity, energy, and load distribution. In this paper, we present a hybrid algorithm, called IMRFO-TS, based on the hybridization of Improved Manta Ray Foraging Optimization (IMRFO) with the Tabu Search (TS) algorithm for solving the UAV placement problem in a smart city. First, the tangential control strategy is incorporated into the original MRFO algorithm to enhance its convergence speed and explore the search space effectively. Second, The TS algorithm is hybridized with the IMRFO algorithm to increase the exploitation capability of IMRFO and improve the best solution (placement quality) obtained after each iteration. The performance of the proposed IMRFO-TS algorithm is validated using 52 benchmarks considering the fitness value, coverage, connectivity, energy consumption, and load distribution parameters. Compared to eight well-known optimization meta-heuristics such as the original MRFO, TS algorithm, Bat Algorithm (BA), Firefly Algorithm (FA), Grey Wolf Optimization (GWO), Sine Cosine Algorithm (SCA), Whale Optimization Algorithm (WOA), and Reptile Search Algorithm (RSA), the results of the experiments revealed the significant superiority of the proposed IMRFO-TS algorithm by obtaining promising solutions (optimal positions of UAVs) in the majority of the cases.

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Section: ) Load Distributionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

A Hybrid Improved Manta Ray Foraging Optimization With Tabu Search Algorithm for Solving the UAV Placement Problem in Smart Cities

et al. 2023

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“…Reference [18] introduced a novel solution for the problem of controller placement in software-defined networks. This work adopts nature-inspired algorithms such as manta ray foraging optimization and salp swarm algorithm for problems in controller placement.…”

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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“…To solve the NP-hard problem, efcient meta-heuristic algorithms such as Particle Swarm Optimization (PSO), FireFly Algorithm (FFA), Varna Based Optimization (VBO), k-means, Grey Wolf Optimization (GWO), Bacterial Foraging Optimization (BFO) and so on for latency measurements criteria have been studied and designed for diferent networks [9].…”

Section: Controller Placement Problem (Cpp)mentioning

confidence: 99%

Multi-Controller Placement Optimization Using Naked Mole-Rat Algorithm over Software-Defined Networking Environment

Sapkota

Dawadi

Joshi

2022

Journal of Computer Networks and Communications

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Software Defined Networking (SDN) is the novel networking paradigm where decoupling of the control plane from the data plane has its inherent advantages. Controller Placement Problem (CPP) involves placing the optimal number of controllers at the appropriate locations while meeting prerequisites such as latency, load balancing, energy and computational time. To achieve scalability, deployment of multiple controllers on large-scale SDN is one of the key challenges. CPP can be addressed as a multi-objective combinatorial optimization problem whose solution is a trade-off between multiple optimization parameters. In this paper, a novel population-based meta-heuristic algorithm viz. Naked Mole-Rat (NMR) Algorithm has been proposed to optimize the location for controller placement based on Switch-Controller (SC), Controller-Controller (CC) latency while maintaining load balancing among the controllers. The ideas and mechanisms are illustrated using two publicly available standard topologies viz. Ernet and Savvis. The controller localization approach implemented with NMR algorithm has slightly a better result as compared with the Bat algorithm.

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A novel controller placement algorithm based on network portioning concept and a hybrid discrete optimization algorithm for multi-controller software-defined networks

Cited by 27 publications

References 64 publications

A Hybrid Improved Manta Ray Foraging Optimization With Tabu Search Algorithm for Solving the UAV Placement Problem in Smart Cities

A Hybrid Improved Manta Ray Foraging Optimization With Tabu Search Algorithm for Solving the UAV Placement Problem in Smart Cities

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

Multi-Controller Placement Optimization Using Naked Mole-Rat Algorithm over Software-Defined Networking Environment

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