This SpringerBrief provides essential insights on the SDN application designing and deployment in distributed datacenters. In this book, three key problems are discussed: SDN application designing, SDN deployment and SDN management. This book demonstrates how to design the SDN-based request allocation application in distributed datacenters. It also presents solutions for SDN controller placement to deploy SDN in distributed datacenters. Finally, an SDN management system is proposed to guarantee the performance of datacenter networks which are covered and controlled by many heterogeneous controllers. Researchers and practitioners alike will find this book a valuable resource for further study on Software Defined Networking.

This book provides comprehensive discussion on key topics related to the usage and deployment of software defined networks (SDN) in Internet of Everything applications like, healthcare systems, data centers, edge/fog computing, vehicular networks, intelligent transportation systems, smart grids, smart cities and more. The authors provide diverse solutions to overcome challenges of conventional network binding in various Internet of Everything applications where there is need of an adaptive, agile, and flexible network backbone. The book showcases different deployment models, algorithms and implementations related to the usage of SDN in Internet of Everything applications along with the pros and cons of the same. Even more, this book provides deep insights into the architecture of software defined networking specifically about the layered architecture and different network planes, logical interfaces, and programmable operations. The need of network virtualization and the deployment models for network function virtualization is also included with an aim towards the design of interoperable network architectures by researchers in future. Uniquely, the authors find hands on practical implementation, deployment scenarios and use cases for various software defined networking architectures in Internet of Everything applications like healthcare networks, Internet of Things, intelligent transportation systems, smart grid, underwater acoustic networks and many more. In the end, design and research challenges, open issues, and future research directions are provided in this book for a wide range of readers

SOFTWARE DEFINED NETWORKS Software defined networking suggests an alternative worldview, one that comes with a new software stack to which this book is organized, with the goal of presenting a top-to-bottom tour of SDN without leaving any significant gaps that the reader might suspect can only be filled with magic or proprietary code. Software defined networking (SDN) is an architecture designed to make a network more flexible and easier to manage. SDN has been widely adopted across data centers, WANs, and access networks and serves as a foundational element of a comprehensive intent-based networking (IBN) architecture. Although SDN has so far been limited to automated provisioning and configuration, IBN now adds “translation” and “assurance” so that the complete network cycle can be automated, continuously aligning the network to business needs. In 14 chapters, this book provides a comprehensive understanding of an SDN-based network as a scalable distributed system running on commodity hardware. The reader will have a one-stop reference looking into the applications, architectures, functionalities, virtualization, security, and privacy challenges connected to SDN. Audience Researchers in software, IT, and electronic engineering as well as industry engineers and technologists working in areas such as network virtualization, Python network programming, CISCO ACI, software defined network, and cloud computing.

Software Defined Networking is revolutionizing the networking world. While the industry transitions to a software-centric architecture, a clear definition of SDN remains murky at best. This book clarifies the current industry confusion about what SDN is, why it's important, and most importantly the protocols and use cases that define SDN. OpenFlow (OF) is a critical piece of the SDN puzzle. While SDN solutions exist that do not require OF, it is undeniable that OF helped spur the innovation in SDN. The history of OF, its current and future status, and the associated use cases will be explained in detail in this book. Lastly, the book attempts to lay out SDN deployments that are real and current today, and apply practicality to the vast world of SDN architectures.

Software-Defined Networks (SDN) work by virtualization of the network and the Cognitive Software-Defined Network (CSDN) combines the efficiencies of SDN with cognitive learning algorithms and enhanced protocols to automatize SDN. Partial deployment of SDN along with traditional networking devices forms a Hybrid Software-Defined Network (HSDN). Software-Defined Network Frameworks: Security Issues and Use Cases consolidates the research relating to the security in SDN, CSDN, and Hybrid SDNs. The security enhancements derived from the use of various SDN frameworks and the security challenges thus introduced, are also discussed. Overall, this book explains the different architectures of SDNs and the security challenges needed for implementing them. Features: Illustrates different frameworks of SDN and their security issues in a single volume Discusses design and assessment of efficient SDN northbound/southbound interfaces Describes cognitive computing, affective computing, machine learning, and other novel tools Illustrates coupling of SDN and traditional networking – Hybrid SDN Explores services, technologies, algorithms, and methods for data analysis in CSDN The book is aimed at researchers and graduate students in software engineering, network security, computer networks, high performance computing, communications engineering, and intelligent systems.

This book highlights several gaps that have not been addressed in existing cyber security research. It first discusses the recent attack prediction techniques that utilize one or more aspects of information to create attack prediction models. The second part is dedicated to new trends on information fusion and their applicability to cyber security; in particular, graph data analytics for cyber security, unwanted traffic detection and control based on trust management software defined networks, security in wireless sensor networks & their applications, and emerging trends in security system design using the concept of social behavioral biometric. The book guides the design of new commercialized tools that can be introduced to improve the accuracy of existing attack prediction models. Furthermore, the book advances the use of Knowledge-based Intrusion Detection Systems (IDS) to complement existing IDS technologies. It is aimed towards cyber security researchers.

Discusses virtual network security concepts Considers proactive security using moving target defense Reviews attack representation models based on attack graphs and attack trees Examines service function chaining in virtual networks with security considerations Recognizes machine learning and AI in network security

Explore the emerging definitions, protocols, and standards for SDN—software-defined, software-driven, programmable networks—with this comprehensive guide. Two senior network engineers show you what’s required for building networks that use software for bi-directional communication between applications and the underlying network infrastructure. This vendor-agnostic book also presents several SDN use cases, including bandwidth scheduling and manipulation, input traffic and triggered actions, as well as some interesting use cases around big data, data center overlays, and network-function virtualization. Discover how enterprises and service providers alike are pursuing SDN as it continues to evolve. Explore the current state of the OpenFlow model and centralized network control Delve into distributed and central control, including data plane generation Examine the structure and capabilities of commercial and open source controllers Survey the available technologies for network programmability Trace the modern data center from desktop-centric to highly distributed models Discover new ways to connect instances of network-function virtualization and service chaining Get detailed information on constructing and maintaining an SDN network topology Examine an idealized SDN framework for controllers, applications, and ecosystems

Emerging machine learning techniques bring new opportunities to flexible network control and management. This book focuses on using state-of-the-art machine learning-based approaches to improve the performance of Software-Defined Networking (SDN). It will apply several innovative machine learning methods (e.g., Deep Reinforcement Learning, Multi-Agent Reinforcement Learning, and Graph Neural Network) to traffic engineering and controller load balancing in software-defined wide area networks, as well as flow scheduling, coflow scheduling, and flow migration for network function virtualization in software-defined data center networks. It helps readers reflect on several practical problems of deploying SDN and learn how to solve the problems by taking advantage of existing machine learning techniques. The book elaborates on the formulation of each problem, explains design details for each scheme, and provides solutions by running mathematical optimization processes, conducting simulated experiments, and analyzing the experimental results.

The book Optical Fiber and Wireless Communications provides a platform for practicing researchers, academics, PhD students, and other scientists to review, plan, design, analyze, evaluate, intend, process, and implement diversiform issues of optical fiber and wireless systems and networks, optical technology components, optical signal processing, and security. The 17 chapters of the book demonstrate capabilities and potentialities of optical communication to solve scientific and engineering problems with varied degrees of complexity.