"Distributed and Cloud Computing," named a 2012 Outstanding Academic Title by the American Library Association's "Choice" publication, explains how to create high-performance, scalable, reliable systems, exposing the design principles, architecture, and innovative applications of parallel, distributed, and cloud computing systems.

Starting with an overview of modern distributed models, the book provides comprehensive coverage of distributed and cloud computing, including:

Facilitating management, debugging, migration, and disaster recovery through virtualization

Clustered systems for research or ecommerce applications

Designing systems as web services

Social networking systems using peer-to-peer computing

Principles of cloud computing using examples from open-source and commercial applications

Using examples from open-source and commercial vendors, the text describes cloud-based systems for research, e-commerce, social networking and more.
Complete coverage of modern distributed computing technology including clusters, the grid, service-oriented architecture, massively parallel processors, peer-to-peer networking, and cloud computing Includes case studies from the leading distributed computing vendors: Amazon, Microsoft, Google, and more Explains how to use virtualization to facilitate management, debugging, migration, and disaster recovery Designed for undergraduate or graduate students taking a distributed systems course each chapter includes exercises and further reading, with lecture slides and more available online"

This book constitutes the refereed post-conference proceedings of the Second BenchCouncil International Federated Intelligent Computing and Block Chain Conferences, FICC 2020, held in Qingdao, China, in October/ November 2020.The 32 full papers and 6 short papers presented were carefully reviewed and selected from 103 submissions. The papers of this volume are organized in topical sections on AI and medical technology; AI and big data; AI and block chain; AI and education technology; and AI and financial technology.

The year is 2142. After a catastrophic environmental disaster a small number of elites have built an organic city called Ecoban. Its very exixtence depends on the work of the "Diggers"; refugees who live outside of the city and mine the wasteland to feed Ecoban's energy consumption. Exploited and fighting for their lives, their only hope is rebel fighter, who must infiltrate the city and escape with secrets, to win the freedom of the slaves and save planet from total destruction.

The first textbook to teach students how to build data analytic solutions on large data sets using cloud-based technologies. This is the first textbook to teach students how to build data analytic solutions on large data sets (specifically in Internet of Things applications) using cloud-based technologies for data storage, transmission and mashup, and AI techniques to analyze this data. This textbook is designed to train college students to master modern cloud computing systems in operating principles, architecture design, machine learning algorithms, programming models and software tools for big data mining, analytics, and cognitive applications. The book will be suitable for use in one-semester computer science or electrical engineering courses on cloud computing, machine learning, cloud programming, cognitive computing, or big data science. The book will also be very useful as a reference for professionals who want to work in cloud computing and data science. Cloud and Cognitive Computing begins with two introductory chapters on fundamentals of cloud computing, data science, and adaptive computing that lay the foundation for the rest of the book. Subsequent chapters cover topics including cloud architecture, mashup services, virtual machines, Docker containers, mobile clouds, IoT and AI, inter-cloud mashups, and cloud performance and benchmarks, with a focus on Google's Brain Project, DeepMind, and X-Lab programs, IBKai HwangM SyNapse, Bluemix programs, cognitive initiatives, and neurocomputers. The book then covers machine learning algorithms and cloud programming software tools and application development, applying the tools in machine learning, social media, deep learning, and cognitive applications. All cloud systems are illustrated with big data and cognitive application examples.