World Scientific Series On Future Computing Paradigms And Applications - Böcker

Software-defined network (SDN) and network function virtualization (NFV) are two technology trends that have revolutionized network management, particularly in highly distributed networks that are used in public, private, or hybrid cloud services. SDN and NFV technologies, when combined, simplify the deployment of network resources, lower capital and operating expenses, and offer greater network flexibility. The increasing usage of NFV is one of the primary factors that make SDN adoption attractive. The integration of these two technologies; SDN and NFV, offer a complementary service, with NFV delivering many of the real services controlled in an SDN. While SDN is focused on the control plane, NFV optimizes the actual network services that manage the data flows. Devices such as routers, firewalls, and VPN terminators are replaced with virtual devices that run on commodity hardware in NFV physical networking. This resembles the 'as-a-service' typical model of cloud services in many aspects. These virtual devices can be accessed on-demand by communication, network, or data center providers.This book illustrates the fundamentals and evolution of SDN and NFV and highlights how these two technologies can be integrated to solve traditional networking problems. In addition, it will focus on the utilization of SDN and NFV to enhance network security, which will open ways to integrate them with current technologies such as IoT, edge computing and blockchain, SDN-based network programmability, and current network orchestration technologies. The basics of SDN and NFV and associated issues, challenges, technological advancements along with advantages and risks of shifting networking paradigm towards SDN are also discussed. Detailed exercises within the book and corresponding solutions are available online as accompanying supplementary material.

With the proliferation of information, big data management and analysis have become an indispensable part of any system to handle such amounts of data. The amount of data generated by the multitude of interconnected devices increases exponentially, making the storage and processing of these data a real challenge.Big data management and analytics have gained momentum in almost every industry, ranging from finance or healthcare. Big data can reveal key insights if handled and analyzed properly; it has great application potential to improve the working of any industry. This book covers the spectrum aspects of big data; from the preliminary level to specific case studies. It will help readers gain knowledge of the big data landscape.Highlights of the topics covered include description of the Big Data ecosystem; real-world instances of big data issues; how the Vs of Big Data (volume, velocity, variety, veracity, valence, and value) affect data collection, monitoring, storage, analysis, and reporting; structural process to get value out of Big Data and recognize the differences between a standard database management system and a big data management system.Readers will gain insights into choice of data models, data extraction, data integration to solve large data problems, data modelling using machine learning techniques, Spark's scalable machine learning techniques, modeling a big data problem into a graph database and performing scalable analytical operations over the graph and different tools and techniques for processing big data and its applications including in healthcare and finance.

Digital images play a significant role in representing useful information; however, they may get distorted while passing through several operational stages, such as image acquisition, compression, transmission, processing, and reconstruction. Similarly, during the transmission of images, some data may be lost and the quality of the image may degrade due to bandwidth limitation. The use of distorted images in any application compromises its performance; and therefore, the identification and quantification of degradation become key issues to address in restoring the distorted images.This book covers different quality assessment techniques for natural images. It further discusses image enhancement techniques that are based on estimated quality. This book presents image quality assessment techniques for different distortions, such as poor contrast, poor illumination, noise and artifacts in deblocked images. Also presented are quality-aware techniques for image enhancement for different distortions.Review and objective questions (with answers) for each chapter will be available online as part of the supplementary material.