Software Defined Networking has been the primary component for Quality of Service provisioning in the last decade. The key idea in such networks is producing independence between the control and the data-plane. The control plane essentially provides decision making logic to the data-plane, which in-turn is only responsible for moving the packets from source to destination based on the flow-table entries and actions. In this thesis an in-depth design and analysis of Software Defined Networking control plane architecture for Next Generation Networks is provided. Typically, Next Generation Networks are those that need to satisfy Quality of Service restrictions (like time bounds, priority, hops, to name a few) before the packets are in transit. For instance, applications that are dependent on prediction popularly known as ML/AI applications have heavy resource requirements and require completion of tasks within the time bounds otherwise the scheduling is rendered useless. The bottleneck could be essentially on any layer of the network stack, however in this thesis the focus is on layer-2 and layer-3 scheduling. To that end, the design of an intelligent control plane is proposed by paying attention to the scheduling, routing and admission strategies which are necessary to facilitate the aforementioned applications requirement. Simulation evaluation and comparisons with state of the art approaches is provided withreasons corroborating the design choices. Finally, quantitative metrics are defined and measured to justify the benefits of the designs.

Ethernet based technologies are emerging as the ubiquitous de facto form of communication due to their interoperability, capacity, cost, and reliability. Traditional Ethernet is designed with the goal of delivering best effort services. However, several real time and control applications require more precise deterministic requirements and Ultra Low Latency (ULL), that Ethernet cannot be used for. Current Industrial Automation and Control Systems (IACS) applications use semi-proprietary technologies that provide deterministic communication behavior for sporadic and periodic traffic, but can lead to closed systems that do not interoperate effectively. The convergence between the informational and operational technologies in modern industrial control networks cannot be achieved using traditional Ethernet. Time Sensitive Networking (TSN) is a suite of IEEE standards designed by augmenting traditional Ethernet with real time deterministic properties ideal for Digital Signal Processing (DSP) applications. Similarly, Deterministic Networking (DetNet) is a Internet Engineering Task Force (IETF) standardization that enhances the network layer with the required deterministic properties needed for IACS applications. This dissertation provides an in-depth survey and literature review on both standards/research and 5G related material on ULL. Recognizing the limitations of several features of the standards, this dissertation provides an empirical evaluation of these approaches and presents novel enhancements to the shapers and schedulers involved in TSN. More specifically, this dissertation investigates Time Aware Shaper (TAS), Asynchronous Traffic Shaper (ATS), and Cyclic Queuing and Forwarding (CQF) schedulers. Moreover, the IEEE 802.1Qcc, centralized management and control, and the IEEE 802.1Qbv can be used to manage and control scheduled traffic streams with periodic properties along with best-effort traffic on the same network infrastructure. Both the centralized network/distributed user model (hybrid model) and the fully-distributed (decentralized) IEEE 802.1Qcc model are examined on a typical industrial control network with the goal of maximizing scheduled traffic streams. Finally, since industrial applications and cyber-physical systems require timely delivery, any channel or node faults can cause severe disruption to the operational continuity of the application. Therefore, the IEEE 802.1CB, Frame Replication and Elimination for Reliability (FRER), is examined and tested using machine learning models to predict faulty scenarios and issue remedies seamlessly.

Nowadays, demand from the Internet of Things (IoT), automotive networking, and video applications is driving the transformation of Ethernet. It is a shift towards time-sensitive Ethernet. As a large amount of data is transmitted, many errors occur in the network. For this increased traffic, a Time Sensitive Network (TSN) is important. Time-Sensitive Network (TSN) is a technology that provides a definitive service for time sensitive traffic in an Ethernet environment that provides time-synchronization. In order to efficiently manage these errors, countermeasures against errors are required. A system that maintains its function even in the event of an internal fault or failure is called a Fault-Tolerant system. For this, after configuring the network environment using the OMNET++ program, machine learning was used to estimate the optimal alternative routing path in case an error occurred in transmission. By setting an alternate path before an error occurs, I propose a method to minimize delay and minimize data loss when an error occurs. Various methods were compared. First, when no replication environment and secondly when ideal replication, thirdly random replication, and lastly replication using ML were tested. In these experiments, replication in an ideal environment showed the best results, which is because everything is optimal. However, except for such an ideal environment, replication prediction using the suggested ML showed the best results. These results suggest that the proposed method is effective, but there may be problems with efficiency and error control, so an additional overview is provided for further improvement.

Existing radio access networks (RANs) allow only for very limited sharing of thecommunication and computation resources among wireless operators and heterogeneous wireless technologies. The introduced LayBack architecture facilitates communication and computation resource sharing among different wireless operators and technologies. LayBack organizes the RAN communication and multiaccess edge computing (MEC) resources into layers, including a devices layer, a radio node (enhanced Node B and access point) layer, and a gateway layer. The layback optimization study addresses the problem of how a central SDN orchestrator can flexibly share the total backhaul capacity of the various wireless operators among their gateways and radio nodes (e.g., LTE enhanced Node Bs or Wi-Fi access points). In order to facilitate flexible network service virtualization and migration, network functions (NFs) are increasingly executed by software modules as so-called "softwarized NFs" on General-Purpose Computing (GPC) platforms and infrastructures. GPC platforms are not specifically designed to efficiently execute NFs with their typically intense Input/Output (I/O) demands. Recently, numerous hardware-based accelerations have been developed to augment GPC platforms and infrastructures, e.g., the central processing unit (CPU) and memory, to efficiently execute NFs. The computing capabilities of client devices are continuously increasing; at the same time, demands for ultra-low latency (ULL) services are increasing. These ULL services can be provided by migrating some micro-service container computations from the cloud and multi-access edge computing (MEC) to the client devices.

Emerging modular cable network architectures distribute some cable headend functions to remote nodes that are located close to the broadcast cable links reaching the cable modems (CMs) in the subscriber homes and businesses. In the Remote- PHY (R-PHY) architecture, a Remote PHY Device (RPD) conducts the physical layer processing for the analog cable transmissions, while the headend runs the DOCSIS medium access control (MAC) for the upstream transmissions of the distributed CMs over the shared cable link. In contrast, in the Remote MACPHY (R-MACPHY) ar- chitecture, a Remote MACPHY Device (RMD) conducts both the physical and MAC layer processing. The dissertation objective is to conduct a comprehensive perfor- mance comparison of the R-PHY and R-MACPHY architectures. Also, development of analytical delay models for the polling-based MAC with Gated bandwidth alloca- tion of Poisson traffic in the R-PHY and R-MACPHY architectures and conducting extensive simulations to assess the accuracy of the analytical model and to evaluate the delay-throughput performance of the R-PHY and R-MACPHY architectures for a wide range of deployment and operating scenarios. Performance evaluations ex- tend to the use of Ethernet Passive Optical Network (EPON) as transport network between remote nodes and headend. The results show that for long CIN distances above 100 miles, the R-MACPHY architecture achieves significantly shorter mean up- stream packet delays than the R-PHY architecture, especially for bursty traffic. The extensive comparative R-PHY and R-MACPHY comparative evaluation can serve as a basis for the planning of modular broadcast cable based access networks.