A Novel Placement Algorithm for the Controllers Of the Virtual Networks (COVN) in SD-WAN with Multiple VNs

The escalation of communication demands and the emergence of new telecommunication concepts such as 5G cellular system and smart cities requires the consolidation of a flexible and manageable backbone network. These requirements motivated the researcher to come up with a new placement algorithm for the Controller of Virtual Network (COVN). This is because SDN and network virtualisation techniques (NFV and NV), are integrated to produce multiple virtual networks running on a single SD-WAN infrastructure, which serves the new backbone. One of the significant challenges of SD-WAN is determining the number and the locations of its controllers to optimise the network latency and reliability. This problem is fairly investigated and solved by several controller placement algorithms where the focus is only on physical controllers. The advent of the sliced SD-WAN produces a new challenge, which necessitates the SDWAN controllers (physical controller/hosted server) to run multiple instances of controllers (virtual controllers). Every virtual network is managed by its virtual controllers. This calls for an algorithm to determine the number and the positions of physical and virtual controllers of the multiple virtual SD-WANs. According to the literature review and to the best of the author knowledge, this problem is neither examined nor yet solved. To address this issue, the researcher designed a novel COVN placement algorithm to compute the controller placement of the physical controllers, then calculate the controller placement of every virtual SD-WAN independently, taking into consideration the controller placement of other virtual SD-WANs. COVN placement does not partition the SD-WAN when placing the physical controllers, unlike all previous placement algorithms. Instead, it identifies the nodes of the optimal reliability and latency to all switches of the network. Then, it partitions every VN separately to create its independent controller placement. COVN placement optimises the reliability and the latency according to the desired weights. It also maintains the load balancing and the optimal resources utilisation. Moreover, it supports the recovering of the controller failure. This novel algorithm is intensively evaluated using the produced COVN simulator and the developed Mininet emulator. The results indicate that COVN placement achieves the required optimisations mentioned above. Also, the implementations disclose that COVN placement can compute the controller placement for a large network ( 754 switches) in very small computation time (49.53 s). In addition, COVN placement is compared to POCO algorithm. The outcome reveals that COVN placement provides better reliability in about 30.76% and a bit higher latency in about 1.38%. Further, it surpasses POCO by constructing the balanced clusters according to the switch loads and offering the more efficient placement to recover controller-failure

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

Defining a network management architecture

This work proposes an algorithm called k-Critical Node to solve the controller placement problem in Software Defined Networks. K-Critical finds the minimum number of controllers to satisfy a target communication delay between controller and nodes, Dreq. In addition, the controller selected create a management architecture that improves the subjacent network performance. In this work we focus on the controllers selection procedure, and show the desired management architecture characteristics that optimize the control and management of the network. The results show that our management trees balance the load among them and reduce the data loss.Peer Reviewe

Defining a network management architecture

This work proposes an algorithm called k-Critical Node to solve the controller placement problem in Software Defined Networks. K-Critical finds the minimum number of controllers to satisfy a target communication delay between controller and nodes, Dreq. In addition, the controller selected create a management architecture that improves the subjacent network performance. In this work we focus on the controllers selection procedure, and show the desired management architecture characteristics that optimize the control and management of the network. The results show that our management trees balance the load among them and reduce the data loss.Peer Reviewe