Adaptive and Scalable Controller Placement in Software-Defined Networking

Software-defined networking (SDN) revolutionizes network control by externalizing and centralizing the control plane. A critical aspect of SDN is Controller Placement (CP), which involves identifying the ideal number and location of controllers in a network to fulfill diverse objectives such as latency constraints (node-to-controller and controller-controller delay), fault tolerance, and controller load. Existing optimization techniques like Multi-Objective Particle Swarm Optimisation (MOPSO), Adapted Non-Dominating Sorting Genetic Algorithm-III (ANSGA-III), and Non-Dominating Sorting Genetic Algorithm-II (NSGA-II) struggle with scalability (except ANSGA-III), computational complexity, and inability to predict the required number of controllers. This thesis proposes two novel approaches to address these challenges. First, an enhanced version of NSGA-III with a repair operator-based approach (referred to as ANSGA-III) is introduced, enabling efficient CP in SD-WAN by optimizing multiple conflicting objectives simultaneously. Second, a Stochastic Computational Graph Model with Ensemble Learning (SCGMEL) is developed, overcoming scalability and computational inefficiency associated with existing methods. SCGMEL employs stochastic gradient descent with momentum, a learning rate decay, a computational graph model, a weighted sum approach, and the XGBoost algorithm for optimization and machine learning. The XGBoost predicts the number of controllers needed and a supervised classification algorithm called Learning Vector Quantization (LVQ) is used to predict the optimal locations of controllers. Additionally, this research introduces the Improved Switch Migration Decision Algorithm (ISMDA) as part of the holistic contribution. ISMDA is implemented on each controller to ensure even load distribution throughout the controllers. It functions as a plug-and-play module, periodically checking if the load surpasses a certain limit. ISMDA improves controller throughput by approximately 7.4% over CAMD and roughly 1.1% over DALB. ISMDA also outperforms DALB and CAMD with a decrease of 5.7% and 1%, respectively, in terms of controller response time. Additionally, ISMDA outperforms DALB and CAMD with a decrease of 1.7% and 5.6%, respectively, in terms of the average frequency of migrations. The established framework results in fewer switch migrations during controller load imbalance. Finally, ISMDA proves more efficient than DALB and CAMD, with an estimated 1% and 6.4% lower average packet loss, respectively. This efficiency is a result of the proposed migration efficiency strategy, allowing ISMDA to handle higher loads and reject fewer packets. Real-world experiments were conducted using the Internet Zoo topology dataset to evaluate the proposed solutions. Six objective functions, including worst-case switch-to-controller delay, load balancing, reliability, average controller-to-controller latency, maximum controller-to-controller delay, and average switch-to-controller delay, were utilized for performance evaluation. Results demonstrated that ANSGA-III outperforms existing algorithms in terms of hypervolume indicator, execution time, convergence, diversity, and scalability. SCGMEL exhibited exceptional computational efficiency, surpassing ANSGA-III, NSGA-II, and MOPSO by 99.983%, 99.985%, and 99.446% respectively. The XGBoost regression model performed significantly better in predicting the number of controllers with a mean absolute error of 1.855751 compared to 3.829268, 3.729883, and 1.883536 for KNN, linear regression, and random forest, respectively. The proposed LVQ-based classification method achieved a test accuracy of 84% and accurately predicted six of the seven controller locations. To culminate, this study presents a refined and intelligent framework designed to optimize Controller Placement (CP) within the context of SD-WAN. The proposed solutions effectively tackle the shortcomings associated with existing algorithms, addressing challenges of scalability, intelligence (including the prediction of optimal controller numbers), and computational efficiency in the pursuit of simultaneous optimization of multiple conflicting objectives. The outcomes underscore the supremacy of the suggested methodologies and underscore their potential transformative influence on SDN deployments. Notably, the findings validate the efficacy of the proposed strategies, ANSGA-III and SCGMEL, in enhancing the optimization of controller placement within SD-WAN setups. The integration of the XGBoost regression model and LVQ-based classification technique yields precise predictions for both optimal controller quantities and their respective positions. Additionally, the ISMDA algorithm emerges as a pivotal enhancement, enhancing controller throughput, mitigating packet losses, and reducing switch migration frequency—collectively contributing to elevated standards in SDN deployments

An Adapted Nondominated Sorting Genetic Algorithm III (NSGA-III) With Repair-Based Operator for Solving Controller Placement Problem in Software-Defined Wide Area Networks

Optimum controller placement in the presence of several conflicting objectives has received significant attention in the Software-Defined Wide Area Network (SD-WAN) deployment. Multi-objective evolutionary algorithms, like Non-dominated Sorting Genetic Algorithm II (NSGA-II) and Multi-objective Particle Swamp Optimization (MOPSO), have proved helpful in solving Controller Placement Problem (CPP) in SD-WAN. However, these algorithms were associated with the challenge of scalability (when there are more than three objectives) for optimization in the SD-WAN. Hence, this study proposed an adapted NSGA-III (A-NSGA-III) to resolve the scalability challenges associated with NSGA-II and MOPSO algorithms in the presence of more than three objectives. This study developed and introduced a repair-based operator into the existing Mechanical Engineering based NSGA-III to propose the A-NSGA-III for optimal controller placement in the SD-WAN. The proposed A-NSGA-III, the NSGA-II and MOPSO algorithms were subjected to evaluation using datasets from Internet2 OS3E WAN topology with six objective functions. The Hypervolume indicator, Percentage Coefficient of Variation (PCV), the percentage difference and the Parallel Coordinate Plots (PCP) confirmed that the proposed A-NSGA-III exhibited high convergence and diversification than the NSGA-II and MOPSO algorithms in the presence of scalability challenge (when the number of objective function exceeded three). The result confirmed that the proposed A-NSGA-III solved the scalability challenges associated with the optimal Controller Placement in the SD-WAN. Hence, A-NSGA-III was recommended over NSGA-II and MOPSO algorithms, subject to the confirmation usage conditions

Adaptive and Scalable Controller Placement in Software-Defined Networking

Software-defined networking (SDN) revolutionizes network control by externalizing and centralizing the control plane. A critical aspect of SDN is Controller Placement (CP), which involves identifying the ideal number and location of controllers in a network to fulfill diverse objectives such as latency constraints (node-to-controller and controller-controller delay), fault tolerance, and controller load. Existing optimization techniques like Multi-Objective Particle Swarm Optimisation (MOPSO), Adapted Non-Dominating Sorting Genetic Algorithm-III (ANSGA-III), and Non-Dominating Sorting Genetic Algorithm-II (NSGA-II) struggle with scalability (except ANSGA-III), computational complexity, and inability to predict the required number of controllers. This thesis proposes two novel approaches to address these challenges. First, an enhanced version of NSGA-III with a repair operator-based approach (referred to as ANSGA-III) is introduced, enabling efficient CP in SD-WAN by optimizing multiple conflicting objectives simultaneously. Second, a Stochastic Computational Graph Model with Ensemble Learning (SCGMEL) is developed, overcoming scalability and computational inefficiency associated with existing methods. SCGMEL employs stochastic gradient descent with momentum, a learning rate decay, a computational graph model, a weighted sum approach, and the XGBoost algorithm for optimization and machine learning. The XGBoost predicts the number of controllers needed and a supervised classification algorithm called Learning Vector Quantization (LVQ) is used to predict the optimal locations of controllers. Additionally, this research introduces the Improved Switch Migration Decision Algorithm (ISMDA) as part of the holistic contribution. ISMDA is implemented on each controller to ensure even load distribution throughout the controllers. It functions as a plug-and-play module, periodically checking if the load surpasses a certain limit. ISMDA improves controller throughput by approximately 7.4% over CAMD and roughly 1.1% over DALB. ISMDA also outperforms DALB and CAMD with a decrease of 5.7% and 1%, respectively, in terms of controller response time. Additionally, ISMDA outperforms DALB and CAMD with a decrease of 1.7% and 5.6%, respectively, in terms of the average frequency of migrations. The established framework results in fewer switch migrations during controller load imbalance. Finally, ISMDA proves more efficient than DALB and CAMD, with an estimated 1% and 6.4% lower average packet loss, respectively. This efficiency is a result of the proposed migration efficiency strategy, allowing ISMDA to handle higher loads and reject fewer packets. Real-world experiments were conducted using the Internet Zoo topology dataset to evaluate the proposed solutions. Six objective functions, including worst-case switch-to-controller delay, load balancing, reliability, average controller-to-controller latency, maximum controller-to-controller delay, and average switch-to-controller delay, were utilized for performance evaluation. Results demonstrated that ANSGA-III outperforms existing algorithms in terms of hypervolume indicator, execution time, convergence, diversity, and scalability. SCGMEL exhibited exceptional computational efficiency, surpassing ANSGA-III, NSGA-II, and MOPSO by 99.983%, 99.985%, and 99.446% respectively. The XGBoost regression model performed significantly better in predicting the number of controllers with a mean absolute error of 1.855751 compared to 3.829268, 3.729883, and 1.883536 for KNN, linear regression, and random forest, respectively. The proposed LVQ-based classification method achieved a test accuracy of 84% and accurately predicted six of the seven controller locations. To culminate, this study presents a refined and intelligent framework designed to optimize Controller Placement (CP) within the context of SD-WAN. The proposed solutions effectively tackle the shortcomings associated with existing algorithms, addressing challenges of scalability, intelligence (including the prediction of optimal controller numbers), and computational efficiency in the pursuit of simultaneous optimization of multiple conflicting objectives. The outcomes underscore the supremacy of the suggested methodologies and underscore their potential transformative influence on SDN deployments. Notably, the findings validate the efficacy of the proposed strategies, ANSGA-III and SCGMEL, in enhancing the optimization of controller placement within SD-WAN setups. The integration of the XGBoost regression model and LVQ-based classification technique yields precise predictions for both optimal controller quantities and their respective positions. Additionally, the ISMDA algorithm emerges as a pivotal enhancement, enhancing controller throughput, mitigating packet losses, and reducing switch migration frequency—collectively contributing to elevated standards in SDN deployments

A Stochastic Computational Graph with Ensemble Learning Model for solving Controller Placement Problem in Software-Defined Wide Area Networks

The Preponderance of literature has established that most of the metaheuristic algorithms were associated with identified challenges in solving the Controller Placement Problem in SD-WAN. This study proposed a Stochastic Computational Graph Model with an Ensemble Learning (SCGMEL) approach to address the scalability, intelligence, and high computational complexity challenges experienced by the existing metaheuristic algorithms. The proposed SCGMEL used stochastic gradient descent with momentum and learning rate decay, a computational graph model, and the eXtreme Gradient Boosted Trees (XGBoost) algorithm as the optimization and machine learning approaches. The proposed solution was tested using datasets from Internet Zoo topology with six objective functions: load balancing, maximum controller failure, average controller-to-controller latency, average switch-to-controller latency, and maximum controller-to-controller latency. The XGBoost outperformed other regression models, in predicting the number of controllers, with mean absolute error of 1.855751 versus 1.883536, 3.729863, and 3.829268 for the random forest, logistic regression, and K-nearest neighbor, respectively. Furthermore, the execution time, average and total CPU usages of the algorithms demonstrated the computational efficiency of the proposed SCGMEL over ANSGA-III, NSGA-II, and MOPSO with percentage decreases of 99.983%, 99.985%, and 99.446%, respectively. Consequently, the proposed SCGMEL was recommended for controller placement in SD-WAN, subject to the usage conditions

The pattern and impact of social media use among Orthodontists and Orthodontic resident doctors in Nigeria

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Dataset on the evaluation of antimicrobial activity and optical properties of green synthesized silver and its allied bimetallic nanoparticles

The pursuit for bioremediation has led to alternative route for the synthesis of nanoparticles and their hybrids. Data in this article display optical properties and progress in the formation of silver and silver/nickel bimetallic nanoparticles using eco-friendly reducing agent (Akinsiku et al. 2018). The as-prepared nanoparticles portrayed nanocrystalline nature as revealed in the x-ray powder diffraction (XRPD) data. Data also exposed antimicrobial activity of the synthesized nanoparticles

Dataset on the evaluation of antimicrobial activity and optical properties of green synthesized silver and its allied bimetallic nanoparticles

The pursuit for bioremediation has led to alternative route for the synthesis of nanoparticles and their hybrids. Data in this article display optical properties and progress in the formation of silver and silver/nickel bimetallic nanoparticles using eco-friendly reducing agent (Akinsiku et al. 2018). The as-prepared nanoparticles portrayed nanocrystalline nature as revealed in the x-ray powder diffraction (XRPD) data. Data also exposed antimicrobial activity of the synthesized nanoparticles