Wiley on Parallel and Distributed Computing

Complex systems modeling and simulation approaches are being adopted in a growing number of sectors, including finance, economics, biology, astronomy, and many more. Technologies ranging from distributed computing to specialized hardware are explored and developed to address the computational requirements arising in complex systems simulations. The aim of this book is to present a representative overview of contemporary large-scale computing technologies in the context of complex systems simulations applications. The intention is to identify new research directions in this field and to provide a communications platform facilitating an exchange of concepts, ideas and needs between the scientists and technologist and complex system modelers. On the application side, the book focuses on modeling and simulation of natural and man-made complex systems. On the computing technology side, emphasis is placed on the distributed computing approaches, but supercomputing and other novel technologies are also considered.

There is a software gap between the hardware potential and the performance that can be attained using today's software parallel program development tools. The tools need manual intervention by the programmer to parallelize the code. Programming a parallel computer requires closely studying the target algorithm or application, more so than in the traditional sequential programming we have all learned. The programmer must be aware of the communication and data dependencies of the algorithm or application. This book provides the techniques to explore the possible ways to program a parallel computer for a given application.

The primary purpose of this book is to capture the state-of-the-art in “Cloud Computing Technologies and Applications”. The book will also aim to identify potential research directions and technologies that will facilitate creation a global marketplace of cloud computing services supporting scientific, industrial, business, and consumer applications. We expect the book to serve as a reference for larger audience such as systems architects, practitioners, developers, new researchers and graduate level students. This area of research is relatively recent, and as such has no existing reference book that addresses it. This book will be a timely contribution to a field that is gaining considerable research interest, momentum, and is expected to be of increasing interest to commercial developers. The book is targeted for professional computer science developers and graduate students especially at Masters level. As Cloud Computing is recognized as one of the top five emerging technologies that will have a major impact on the quality of science and society over the next 20 years, its knowledge will help position our readers at the forefront of the field.

Complete guidance for mastering the tools and techniques of the digital revolution. With the digital revolution opening up tremendous opportunities in many fields, there is a growing need for skilled professionals who can develop data-intensive systems and extract information and knowledge from them. This book frames for the first time a new systematic approach for tackling the challenges of data-intensive computing, providing decision makers and technical experts alike with practical tools for dealing with our exploding data collections. Emphasizing data-intensive thinking and interdisciplinary collaboration, “The Data Bonanza: Improving Knowledge Discovery in Science, Engineering, and Business” examines the essential components of knowledge discovery, surveys many of the current research efforts worldwide, and points to new areas for innovation. Complete with a wealth of examples and DISPEL-based methods demonstrating how to gain more from data in real-world systems, the book: • Outlines the concepts and rationale for implementing data-intensive computing in organizations • Covers from the ground up problem-solving strategies for data analysis in a data-rich world • Introduces techniques for data-intensive engineering using the Data-Intensive Systems Process Engineering Language DISPEL • Features in-depth case studies in customer relations, environmental hazards, seismology, and more • Showcases successful applications in areas ranging from astronomy and the humanities to transport engineering • Includes sample program snippets throughout the text as well as additional materials on a companion website “The Data Bonanza” is a must-have guide for information strategists, data analysts, and engineers in business, research, and government, and for anyone wishing to be on the cutting edge of data mining, machine learning, databases, distributed systems, or large-scale computing.

With recent changes in multicore and general-purpose computing on graphics processing units, the way parallel computers are used and programmed has drastically changed. It is important to provide a comprehensive study on how to use such machines written by specialists of the domain. The book provides recent research results in high-performance computing on complex environments, information on how to efficiently exploit heterogeneous and hierarchical architectures and distributed systems, detailed studies on the impact of applying heterogeneous computing practices to real problems, and applications varying from remote sensing to tomography. The content spans topics such as Numerical Analysis for Heterogeneous and Multicore Systems; Optimization of Communication for High Performance Heterogeneous and Hierarchical Platforms; Efficient Exploitation of Heterogeneous Architectures, Hybrid CPU+GPU, and Distributed Systems; Energy Awareness in High-Performance Computing; and Applications of Heterogeneous High-Performance Computing. • Covers cutting-edge research in HPC on complex environments, following an international collaboration of members of the ComplexHPC • Explains how to efficiently exploit heterogeneous and hierarchical architectures and distributed systems • Twenty-three chapters and over 100 illustrations cover domains such as numerical analysis, communication and storage, applications, GPUs and accelerators, and energy efficiency

Summarizes the current state and upcoming trends within the area of fog computing. Written by some of the leading experts in the field, Fog Computing: Theory and Practice focuses on the technological aspects of employing fog computing in various application domains, such as smart healthcare, industrial process control and improvement, smart cities, and virtual learning environments. In addition, the Machine-to-Machine (M2M) communication methods for fog computing environments are covered in depth. Presented in two parts-Fog Computing Systems and Architectures, and Fog Computing Techniques and Application-this book covers such important topics as energy efficiency and Quality of Service (QoS) issues, reliability and fault tolerance, load balancing, and scheduling in fog computing systems. It also devotes special attention to emerging trends and the industry needs associated with utilizing the mobile edge computing, Internet of Things (IoT), resource and pricing estimation, and virtualization in the fog environments. • Includes chapters on deep learning, mobile edge computing, smart grid, and intelligent transportation systems beyond the theoretical and foundational concepts • Explores real-time traffic surveillance from video streams and interoperability of fog computing architectures • Presents the latest research on data quality in the IoT, privacy, security, and trust issues in fog computing “Fog Computing: Theory and Practice” provides a platform for researchers, practitioners, and graduate students from computer science, computer engineering, and various other disciplines to gain a deep understanding of fog computing.

A comprehensive guide to Fog and Edge applications, architectures, and technologies Recent years have seen the explosive growth of the Internet of Things (IoT): the internet-connected network of devices that includes everything from personal electronics and home appliances to automobiles and industrial machinery. Responding to the ever-increasing bandwidth demands of the IoT, Fog and Edge computing concepts have developed to collect, analyze, and process data more efficiently than traditional cloud architecture. “Fog and Edge Computing: Principles and Paradigms” provides a comprehensive overview of the state-of-the-art applications and architectures driving this dynamic field of computing while highlighting potential research directions and emerging technologies. Exploring topics such as developing scalable architectures, moving from closed systems to open systems, and ethical issues rising from data sensing, this timely book addresses both the challenges and opportunities that Fog and Edge computing presents. Contributions from leading IoT experts discuss federating Edge resources, middleware design issues, data management and predictive analysis, smart transportation and surveillance applications, and more. A coordinated and integrated presentation of topics helps readers gain thorough knowledge of the foundations, applications, and issues that are central to Fog and Edge computing. This valuable resource: • Provides insights on transitioning from current Cloud-centric and 4G/5G wireless environments to Fog Computing • Examines methods to optimize virtualized, pooled, and shared resources • Identifies potential technical challenges and offers suggestions for possible solutions • Discusses major components of Fog and Edge computing architectures such as middleware, interaction protocols, and autonomic management • Includes access to a website portal for advanced online resources “Fog and Edge Computing: Principles and Paradigms” is an essential source of up-to-date information for systems architects, developers, researchers, and advanced undergraduate and graduate students in fields of computer science and engineering.

Examines the design and use of Intrusion Detection Systems (IDS) to secure Supervisory Control and Data Acquisition (SCADA) systems. Cyber-attacks on SCADA systems-the control system architecture that uses computers, networked data communications, and graphical user interfaces for high-level process supervisory management-can lead to costly financial consequences or even result in loss of life. Minimizing potential risks and responding to malicious actions requires innovative approaches for monitoring SCADA systems and protecting them from targeted attacks. “SCADA Security: Machine Learning Concepts for Intrusion Detection and Prevention” is designed to help security and networking professionals develop and deploy accurate and effective Intrusion Detection Systems (IDS) for SCADA systems that leverage autonomous machine learning. Providing expert insights, practical advice, and up-to-date coverage of developments in SCADA security, this authoritative guide presents a new approach for efficient unsupervised IDS driven by SCADA-specific data. Organized into eight in-depth chapters, the text first discusses how traditional IT attacks can also be possible against SCADA, and describes essential SCADA concepts, systems, architectures, and main components. Following chapters introduce various SCADA security frameworks and approaches, including evaluating security with virtualization-based SCADAVT, using SDAD to extract proximity-based detection, finding a global and efficient anomaly threshold with GATUD, and more. This important book: • Provides diverse perspectives on establishing an efficient IDS approach that can be implemented in SCADA systems • Describes the relationship between main components and three generations of SCADA systems • Explains the classification of a SCADA IDS based on its architecture and implementation • Surveys the current literature in the field and suggests possible directions for future research “SCADA Security: Machine Learning Concepts for Intrusion Detection and Prevention” is a must-read for all SCADA security and networking researchers, engineers, system architects, developers, managers, lecturers, and other SCADA security industry practitioners.

Defines the notion of an activity model learned from sensor data and presents key algorithms that form the core of the field. “Activity Learning: Discovering, Recognizing and Predicting Human Behavior from Sensor Data” provides an in-depth look at computational approaches to activity learning from sensor data. Each chapter is constructed to provide practical, step-by-step information on how to analyze and process sensor data. The book discusses techniques for activity learning that include the following: • Discovering activity patterns that emerge from behavior-based sensor data • Recognizing occurrences of predefined or discovered activities in real time • Predicting the occurrences of activities The techniques covered can be applied to numerous fields, including security, telecommunications, healthcare, smart grids, and home automation. An online companion site enables readers to experiment with the techniques described in the book, and to adapt or enhance the techniques for their own use. With an emphasis on computational approaches, “Activity Learning: Discovering, Recognizing, and Predicting Human Behavior from Sensor Data” provides graduate students and researchers with an algorithmic perspective to activity learning.

Addresses innovations in technology relating to the energy efficiency of a wide variety of contemporary computer systems and networks. With concerns about global energy consumption at an all-time high, improving computer networks energy efficiency is becoming an increasingly important topic. “Large-Scale Distributed Systems and Energy Efficiency: A Holistic View” addresses innovations in technology relating to the energy efficiency of a wide variety of contemporary computer systems and networks. After an introductory overview of the energy demands of current Information and Communications Technology (ICT), individual chapters offer in-depth analyses of such topics as cloud computing, green networking (both wired and wireless), mobile computing, power modeling, the rise of green data centers and high-performance computing, resource allocation, and energy efficiency in peer-to-peer (P2P) computing networks. • Discusses measurement and modeling of the energy consumption method • Includes methods for energy consumption reduction in diverse computing environments • Features a variety of case studies and examples of energy reduction and assessment “Timely and important, Large-Scale Distributed Systems and Energy Efficiency” is an invaluable resource for ways of increasing the energy efficiency of computing systems and networks while simultaneously reducing the carbon footprint.

A highly accessible reference offering a broad range of topics and insights on large scale network-centric distributed systems. Evolving from the fields of high-performance computing and networking, large scale network-centric distributed systems continues to grow as one of the most important topics in computing and communication and many interdisciplinary areas. Dealing with both wired and wireless networks, this book focuses on the design and performance issues of such systems. “Large Scale Network-Centric Distributed Systems” provides in-depth coverage ranging from ground-level hardware issues (such as buffer organization, router delay, and flow control) to the high-level issues immediately concerning application or system users (including parallel programming, middleware, and OS support for such computing systems). Arranged in five parts, it explains and analyzes complex topics to an unprecedented degree: • Part 1: Multicore and Many-Core (Mc) Systems-on-Chip • Part 2: Pervasive/Ubiquitous Computing and Peer-to-Peer Systems • Part 3: Wireless/Mobile Networks • Part 4: Grid and Cloud Computing • Part 5: Other Topics Related to Network-Centric Computing and Its Applications “Large Scale Network-Centric Distributed Systems” is an incredibly useful resource for practitioners, postgraduate students, postdocs, and researchers.

Provides state-of-the-art methods for programming multi-core and many-core systems. The book comprises a selection of twenty-two chapters covering: fundamental techniques and algorithms; programming approaches; methodologies and frameworks; scheduling and management; testing and evaluation methodologies; and case studies for programming multi-core and many-core systems. Program development for multi-core processors, especially for heterogeneous multi-core processors, is significantly more complex than for single-core processors. However, programmers have been traditionally trained for the development of sequential programs, and only a small percentage of them have experience with parallel programming. In the past, only a relatively small group of programmers interested in High Performance Computing (HPC) was concerned with the parallel programming issues, but the situation has changed dramatically with the appearance of multi-core processors on commonly used computing systems. It is expected that with the pervasiveness of multi-core processors, parallel programming will become mainstream. The pervasiveness of multi-core processors affects a large spectrum of systems, from embedded and general-purpose, to high-end computing systems. This book assists programmers in mastering the efficient programming of multi-core systems, which is of paramount importance for the software-intensive industry towards a more effective product-development cycle. Key features: • Lessons, challenges, and roadmaps ahead. • Contains real world examples and case studies. • Helps programmers in mastering the efficient programming of multi-core and many-core systems. The book serves as a reference for a larger audience of practitioners, young researchers and graduate level students. A basic level of programming knowledge is required to use this book.

While other books on the market provide limited coverage of advanced CDNs and streaming technologies, concentrating solely on the fundamentals, this book provides an up-to-date comprehensive coverage of the state-of-the-art advancements in CDNs, with a special focus on Cloud-based CDNs. The book includes CDN and media streaming basics, performance models, practical applications, and business analysis. It features industry case studies, CDN applications, and open research issues to aid practitioners and researchers, and a market analysis to provide a reference point for commercial entities. The book covers Adaptive Bitrate Streaming (ABR), Content Delivery Cloud (CDC), Web Acceleration, Front End Optimization (FEO), Transparent Caching, Next Generation CDNs, CDN Business Intelligence and more. • Provides an in-depth look at Cloud-based CDNs • Includes CDN and streaming media basics and tutorials • Aimed to instruct systems architects, practitioners, product developers, and researchers • Material is divided into introductory subjects, advanced content, and specialist areas

The energy consumption issue in distributed computing systems raises various monetary, environmental and system performance concerns. Electricity consumption in the US doubled from 2000 to 2005. From a financial and environmental standpoint, reducing the consumption of electricity is important, yet these reforms must not lead to performance degradation of the computing systems. These contradicting constraints create a suite of complex problems that need to be resolved in order to lead to 'greener' distributed computing systems. This book brings together a group of outstanding researchers that investigate the different facets of green and energy efficient distributed computing. Key features: • One of the first books of its kind • Features latest research findings on emerging topics by well-known scientists • Valuable research for grad students, postdocs, and researchers • Research will greatly feed into other technologies and application domains.