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| cloud computing textbook by kai hwang: Cloud Computing for Machine Learning and Cognitive Applications Kai Hwang, 2017-06-16 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. |
| cloud computing textbook by kai hwang: Distributed and Cloud Computing Kai Hwang, Jack Dongarra, Geoffrey C. Fox, 2013-12-18 Distributed and Cloud Computing: From Parallel Processing to the Internet of Things offers complete coverage of modern distributed computing technology including clusters, the grid, service-oriented architecture, massively parallel processors, peer-to-peer networking, and cloud computing. It is the first modern, up-to-date distributed systems textbook; it explains how to create high-performance, scalable, reliable systems, exposing the design principles, architecture, and innovative applications of parallel, distributed, and cloud computing systems. Topics covered by this book include: facilitating management, debugging, migration, and disaster recovery through virtualization; clustered systems for research or ecommerce applications; designing systems as web services; and social networking systems using peer-to-peer computing. The principles of cloud computing are discussed using examples from open-source and commercial applications, along with case studies from the leading distributed computing vendors such as Amazon, Microsoft, and Google. Each chapter includes exercises and further reading, with lecture slides and more available online. This book will be ideal for students taking a distributed systems or distributed computing class, as well as for professional system designers and engineers looking for a reference to the latest distributed technologies including cloud, P2P and grid computing. - 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 |
| cloud computing textbook by kai hwang: Big-Data Analytics for Cloud, IoT and Cognitive Computing Kai Hwang, Min Chen, 2017-03-17 The definitive guide to successfully integrating social, mobile, Big-Data analytics, cloud and IoT principles and technologies The main goal of this book is to spur the development of effective big-data computing operations on smart clouds that are fully supported by IoT sensing, machine learning and analytics systems. To that end, the authors draw upon their original research and proven track record in the field to describe a practical approach integrating big-data theories, cloud design principles, Internet of Things (IoT) sensing, machine learning, data analytics and Hadoop and Spark programming. Part 1 focuses on data science, the roles of clouds and IoT devices and frameworks for big-data computing. Big data analytics and cognitive machine learning, as well as cloud architecture, IoT and cognitive systems are explored, and mobile cloud-IoT-interaction frameworks are illustrated with concrete system design examples. Part 2 is devoted to the principles of and algorithms for machine learning, data analytics and deep learning in big data applications. Part 3 concentrates on cloud programming software libraries from MapReduce to Hadoop, Spark and TensorFlow and describes business, educational, healthcare and social media applications for those tools. The first book describing a practical approach to integrating social, mobile, analytics, cloud and IoT (SMACT) principles and technologies Covers theory and computing techniques and technologies, making it suitable for use in both computer science and electrical engineering programs Offers an extremely well-informed vision of future intelligent and cognitive computing environments integrating SMACT technologies Fully illustrated throughout with examples, figures and approximately 150 problems to support and reinforce learning Features a companion website with an instructor manual and PowerPoint slides www.wiley.com/go/hwangIOT Big-Data Analytics for Cloud, IoT and Cognitive Computing satisfies the demand among university faculty and students for cutting-edge information on emerging intelligent and cognitive computing systems and technologies. Professionals working in data science, cloud computing and IoT applications will also find this book to be an extremely useful working resource. |
| cloud computing textbook by kai hwang: Computer Architecture and Parallel Processing Kai Hwang, 1985 |
| cloud computing textbook by kai hwang: Cloud Computing for Science and Engineering Ian Foster, Dennis B. Gannon, 2017-09-29 A guide to cloud computing for students, scientists, and engineers, with advice and many hands-on examples. The emergence of powerful, always-on cloud utilities has transformed how consumers interact with information technology, enabling video streaming, intelligent personal assistants, and the sharing of content. Businesses, too, have benefited from the cloud, outsourcing much of their information technology to cloud services. Science, however, has not fully exploited the advantages of the cloud. Could scientific discovery be accelerated if mundane chores were automated and outsourced to the cloud? Leading computer scientists Ian Foster and Dennis Gannon argue that it can, and in this book offer a guide to cloud computing for students, scientists, and engineers, with advice and many hands-on examples. The book surveys the technology that underpins the cloud, new approaches to technical problems enabled by the cloud, and the concepts required to integrate cloud services into scientific work. It covers managing data in the cloud, and how to program these services; computing in the cloud, from deploying single virtual machines or containers to supporting basic interactive science experiments to gathering clusters of machines to do data analytics; using the cloud as a platform for automating analysis procedures, machine learning, and analyzing streaming data; building your own cloud with open source software; and cloud security. The book is accompanied by a website, Cloud4SciEng.org, that provides a variety of supplementary material, including exercises, lecture slides, and other resources helpful to readers and instructors. |
| cloud computing textbook by kai hwang: Cloud Computing for Machine Learning and Cognitive Applications Kai Hwang, 2017-07-07 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. |
| cloud computing textbook by kai hwang: Computer Arithmetic Kai Hwang, 1979 |
| cloud computing textbook by kai hwang: Mastering Cloud Computing Rajkumar Buyya, Christian Vecchiola, S.Thamarai Selvi, 2013-04-05 Mastering Cloud Computing is designed for undergraduate students learning to develop cloud computing applications. Tomorrow's applications won't live on a single computer but will be deployed from and reside on a virtual server, accessible anywhere, any time. Tomorrow's application developers need to understand the requirements of building apps for these virtual systems, including concurrent programming, high-performance computing, and data-intensive systems. The book introduces the principles of distributed and parallel computing underlying cloud architectures and specifically focuses on virtualization, thread programming, task programming, and map-reduce programming. There are examples demonstrating all of these and more, with exercises and labs throughout. - Explains how to make design choices and tradeoffs to consider when building applications to run in a virtual cloud environment - Real-world case studies include scientific, business, and energy-efficiency considerations |
| cloud computing textbook by kai hwang: Handbook on Data Centers Samee U. Khan, Albert Y. Zomaya, 2015-03-16 This handbook offers a comprehensive review of the state-of-the-art research achievements in the field of data centers. Contributions from international, leading researchers and scholars offer topics in cloud computing, virtualization in data centers, energy efficient data centers, and next generation data center architecture. It also comprises current research trends in emerging areas, such as data security, data protection management, and network resource management in data centers. Specific attention is devoted to industry needs associated with the challenges faced by data centers, such as various power, cooling, floor space, and associated environmental health and safety issues, while still working to support growth without disrupting quality of service. The contributions cut across various IT data technology domains as a single source to discuss the interdependencies that need to be supported to enable a virtualized, next-generation, energy efficient, economical, and environmentally friendly data center. This book appeals to a broad spectrum of readers, including server, storage, networking, database, and applications analysts, administrators, and architects. It is intended for those seeking to gain a stronger grasp on data center networks: the fundamental protocol used by the applications and the network, the typical network technologies, and their design aspects. The Handbook of Data Centers is a leading reference on design and implementation for planning, implementing, and operating data center networks. |
| cloud computing textbook by kai hwang: Cloud Computing: A Hands-On Approach Arshdeep Bahga, Vijay Madisetti, 2013-12-09 About the Book Recent industry surveys expect the cloud computing services market to be in excess of $20 billion and cloud computing jobs to be in excess of 10 million worldwide in 2014 alone. In addition, since a majority of existing information technology (IT) jobs is focused on maintaining legacy in-house systems, the demand for these kinds of jobs is likely to drop rapidly if cloud computing continues to take hold of the industry. However, there are very few educational options available in the area of cloud computing beyond vendor-specific training by cloud providers themselves. Cloud computing courses have not found their way (yet) into mainstream college curricula. This book is written as a textbook on cloud computing for educational programs at colleges. It can also be used by cloud service providers who may be interested in offering a broader perspective of cloud computing to accompany their own customer and employee training programs. The typical reader is expected to have completed a couple of courses in programming using traditional high-level languages at the college-level, and is either a senior or a beginning graduate student in one of the science, technology, engineering or mathematics (STEM) fields. We have tried to write a comprehensive book that transfers knowledge through an immersive hands-on approach, where the reader is provided the necessary guidance and knowledge to develop working code for real-world cloud applications. Additional support is available at the book's website: www.cloudcomputingbook.info Organization The book is organized into three main parts. Part I covers technologies that form the foundations of cloud computing. These include topics such as virtualization, load balancing, scalability & elasticity, deployment, and replication. Part II introduces the reader to the design & programming aspects of cloud computing. Case studies on design and implementation of several cloud applications in the areas such as image processing, live streaming and social networks analytics are provided. Part III introduces the reader to specialized aspects of cloud computing including cloud application benchmarking, cloud security, multimedia applications and big data analytics. Case studies in areas such as IT, healthcare, transportation, networking and education are provided. |
| cloud computing textbook by kai hwang: Introduction to Cloud Computing , |
| cloud computing textbook by kai hwang: Big-Data Analytics for Cloud, IoT and Cognitive Computing Kai Hwang, Min Chen, 2017-08-14 The definitive guide to successfully integrating social, mobile, Big-Data analytics, cloud and IoT principles and technologies The main goal of this book is to spur the development of effective big-data computing operations on smart clouds that are fully supported by IoT sensing, machine learning and analytics systems. To that end, the authors draw upon their original research and proven track record in the field to describe a practical approach integrating big-data theories, cloud design principles, Internet of Things (IoT) sensing, machine learning, data analytics and Hadoop and Spark programming. Part 1 focuses on data science, the roles of clouds and IoT devices and frameworks for big-data computing. Big data analytics and cognitive machine learning, as well as cloud architecture, IoT and cognitive systems are explored, and mobile cloud-IoT-interaction frameworks are illustrated with concrete system design examples. Part 2 is devoted to the principles of and algorithms for machine learning, data analytics and deep learning in big data applications. Part 3 concentrates on cloud programming software libraries from MapReduce to Hadoop, Spark and TensorFlow and describes business, educational, healthcare and social media applications for those tools. The first book describing a practical approach to integrating social, mobile, analytics, cloud and IoT (SMACT) principles and technologies Covers theory and computing techniques and technologies, making it suitable for use in both computer science and electrical engineering programs Offers an extremely well-informed vision of future intelligent and cognitive computing environments integrating SMACT technologies Fully illustrated throughout with examples, figures and approximately 150 problems to support and reinforce learning Features a companion website with an instructor manual and PowerPoint slides www.wiley.com/go/hwangIOT Big-Data Analytics for Cloud, IoT and Cognitive Computing satisfies the demand among university faculty and students for cutting-edge information on emerging intelligent and cognitive computing systems and technologies. Professionals working in data science, cloud computing and IoT applications will also find this book to be an extremely useful working resource. |
| cloud computing textbook by kai hwang: Python Data Cleaning Cookbook Michael Walker, 2020-12-11 Discover how to describe your data in detail, identify data issues, and find out how to solve them using commonly used techniques and tips and tricks Key features Get well-versed with various data cleaning techniques to reveal key insights Manipulate data of different complexities to shape them into the right form as per your business needs Clean, monitor, and validate large data volumes to diagnose problems before moving on to data analysis Book Description Getting clean data to reveal insights is essential, as directly jumping into data analysis without proper data cleaning may lead to incorrect results. This book shows you tools and techniques that you can apply to clean and handle data with Python. You'll begin by getting familiar with the shape of data by using practices that can be deployed routinely with most data sources. Then, the book teaches you how to manipulate data to get it into a useful form. You'll also learn how to filter and summarize data to gain insights and better understand what makes sense and what does not, along with discovering how to operate on data to address the issues you've identified. Moving on, you'll perform key tasks, such as handling missing values, validating errors, removing duplicate data, monitoring high volumes of data, and handling outliers and invalid dates. Next, you'll cover recipes on using supervised learning and Naive Bayes analysis to identify unexpected values and classification errors, and generate visualizations for exploratory data analysis (EDA) to visualize unexpected values. Finally, you'll build functions and classes that you can reuse without modification when you have new data. By the end of this Python book, you'll be equipped with all the key skills that you need to clean data and diagnose problems within it. What you will learn Find out how to read and analyze data from a variety of sources Produce summaries of the attributes of data frames, columns, and rows Filter data and select columns of interest that satisfy given criteria Address messy data issues, including working with dates and missing values Improve your productivity in Python pandas by using method chaining Use visualizations to gain additional insights and identify potential data issues Enhance your ability to learn what is going on in your data Build user-defined functions and classes to automate data cleaning Who this book is for This book is for anyone looking for ways to handle messy, duplicate, and poor data using different Python tools and techniques. The book takes a recipe-based approach to help you to learn how to clean and manage data. Working knowledge of Python programming is all you need to get the most out of the book. |
| cloud computing textbook by kai hwang: Advanced Computer Architecture KAI. HWANG, 2010 |
| cloud computing textbook by kai hwang: Proceedings of the First International Conference on Intelligent Computing and Communication Jyotsna Kumar Mandal, Suresh Chandra Satapathy, Manas Kumar Sanyal, Vikrant Bhateja, 2016-11-21 The book covers a wide range of topics in Computer Science and Information Technology including swarm intelligence, artificial intelligence, evolutionary algorithms, and bio-inspired algorithms. It is a collection of papers presented at the First International Conference on Intelligent Computing and Communication (ICIC2) 2016. The prime areas of the conference are Intelligent Computing, Intelligent Communication, Bio-informatics, Geo-informatics, Algorithm, Graphics and Image Processing, Graph Labeling, Web Security, Privacy and e-Commerce, Computational Geometry, Service Orient Architecture, and Data Engineering. |
| cloud computing textbook by kai hwang: Cloud Enterprise Architecture Pethuru Raj, 2012-10-24 Cloud Enterprise Architecture examines enterprise architecture (EA) in the context of the surging popularity of Cloud computing. It explains the different kinds of desired transformations the architectural blocks of EA undergo in light of this strategically significant convergence. Chapters cover each of the contributing architectures of EA—business, information, application, integration, security, and technology—illustrating the current and impending implications of the Cloud on each. Discussing the implications of the Cloud paradigm on EA, the book details the perceptible and positive changes that will affect EA design, governance, strategy, management, and sustenance. The author ties these topics together with chapters on Cloud integration and composition architecture. He also examines the Enterprise Cloud, Federated Clouds, and the vision to establish the InterCloud. Laying out a comprehensive strategy for planning and executing Cloud-inspired transformations, the book: Explains how the Cloud changes and affects enterprise architecture design, governance, strategy, management, and sustenance Presents helpful information on next-generation Cloud computing Describes additional architectural types such as enterprise-scale integration, security, management, and governance architectures This book is an ideal resource for enterprise architects, Cloud evangelists and enthusiasts, and Cloud application and service architects. Cloud center administrators, Cloud business executives, managers, and analysts will also find the book helpful and inspirational while formulating appropriate mechanisms and schemes for sound modernization and migration of traditional applications to Cloud infrastructures and platforms. |
| cloud computing textbook by kai hwang: Mastering Linux System Administration Christine Bresnahan, Richard Blum, 2021-06-29 Achieve Linux system administration mastery with time-tested and proven techniques In Mastering Linux System Administration, Linux experts and system administrators Christine Bresnahan and Richard Blum deliver a comprehensive roadmap to go from Linux beginner to expert Linux system administrator with a learning-by-doing approach. Organized by do-it-yourself tasks, the book includes instructor materials like a sample syllabus, additional review questions, and slide decks. Amongst the practical applications of the Linux operating system included within, you'll find detailed and easy-to-follow instruction on: Installing Linux servers, understanding the boot and initialization processes, managing hardware, and working with networks Accessing the Linux command line, working with the virtual directory structure, and creating shell scripts to automate administrative tasks Managing Linux user accounts, system security, web and database servers, and virtualization environments Perfect for entry-level Linux system administrators, as well as system administrators familiar with Windows, Mac, NetWare, or other UNIX systems, Mastering Linux System Administration is a must-read guide to manage and secure Linux servers. |
| cloud computing textbook by kai hwang: The Good Son You-Jeong Jeong, 2018-06-05 “Ingeniously twisted.” —Entertainment Weekly, “Must List” “Will leave even the most seasoned crime fiction readers guessing.” —CrimeReads [Jeong] maintains suspense about her inhuman-seeming protagonist's fate until the bitter end.” —The Wall Street Journal Finalist for The Tonight Show Starring Jimmy Fallon’s “Summer Reads” Book Club The Talented Mr. Ripley meets The Bad Seed in this breathless, chilling psychological thriller by the #1 bestselling novelist known as “Korea's Stephen King” Who can you trust if you can't trust yourself? Early one morning, twenty-six-year-old Yu-jin wakes up to a strange metallic smell, and a phone call from his brother asking if everything's all right at home – he missed a call from their mother in the middle of the night. Yu-jin soon discovers her murdered body, lying in a pool of blood at the bottom of the stairs of their stylish Seoul duplex. He can't remember much about the night before; having suffered from seizures for most of his life, Yu-jin often has trouble with his memory. All he has is a faint impression of his mother calling his name. But was she calling for help? Or begging for her life? Thus begins Yu-jin's frantic three-day search to uncover what happened that night, and to finally learn the truth about himself and his family. A shocking and addictive psychological thriller, The Good Son explores the mysteries of mind and memory, and the twisted relationship between a mother and son, with incredible urgency. Named a Must-Read Book of the Summer by Elle, Entertainment Weekly, Vulture, Bustle, CrimeReads, Lit Hub, The Millions, Electric Literature, and Brit + Co |
| cloud computing textbook by kai hwang: Fast and Scalable Cloud Data Management Felix Gessert, Wolfram Wingerath, Norbert Ritter, 2020-05-15 The unprecedented scale at which data is both produced and consumed today has generated a large demand for scalable data management solutions facilitating fast access from all over the world. As one consequence, a plethora of non-relational, distributed NoSQL database systems have risen in recent years and today’s data management system landscape has thus become somewhat hard to overlook. As another consequence, complex polyglot designs and elaborate schemes for data distribution and delivery have become the norm for building applications that connect users and organizations across the globe – but choosing the right combination of systems for a given use case has become increasingly difficult as well. To help practitioners stay on top of that challenge, this book presents a comprehensive overview and classification of the current system landscape in cloud data management as well as a survey of the state-of-the-art approaches for efficient data distribution and delivery to end-user devices. The topics covered thus range from NoSQL storage systems and polyglot architectures (backend) over distributed transactions and Web caching (network) to data access and rendering performance in the client (end-user). By distinguishing popular data management systems by data model, consistency guarantees, and other dimensions of interest, this book provides an abstract framework for reasoning about the overall design space and the individual positions claimed by each of the systems therein. Building on this classification, this book further presents an application-driven decision guidance tool that breaks the process of choosing a set of viable system candidates for a given application scenario down into a straightforward decision tree. |
| cloud computing textbook by kai hwang: GPU Computing Gems Jade Edition Wen-mei Hwu, 2011-09-28 Since the introduction of CUDA in 2007, more than 100 million computers with CUDA capable GPUs have been shipped to end users. GPU computing application developers can now expect their application to have a mass market. With the introduction of OpenCL in 2010, researchers can now expect to develop GPU applications that can run on hardware from multiple vendors-- |
| cloud computing textbook by kai hwang: Introduction to Grid and Cloud Computing Dr. R. Deepalakshmi, Dr. P. Alli, M. Shyni Beaulah, 2017-01-01 This book deals with Anna University Regulation 2013 for the Syllabus CS 6703 Introduction to Grid and Cloud Computing. There are Five units covered in this book. Following are the unit plan of the book. UNIT I INTRODUCTION Evolution of Distributed computing: Scalable computing over the Internet – Technologies for network based systems – clusters of cooperative computers – Grid computing Infrastructures – cloud computing – service oriented architecture – Introduction to Grid Architecture and standards – Elements of Grid – Overview of Grid Architecture. UNIT II GRID SERVICES – Introduction to Open Grid Services Architecture (OGSA) – Motivation – Functionality Requirements – Practical & Detailed view of OGSA/OGSI – Data intensive grid service models – OGSA services. UNIT III VIRTUALIZATION – Cloud deployment models: public, private, hybrid, community – Categories of cloud computing: Everything as a service: Infrastructure, platform, software – Pros and Cons of cloud computing – Implementation levels of virtualization – virtualization structure – virtualization of CPU, Memory and I/O devices – virtual clusters and Resource Management – Virtualization for data center automation. UNIT IV PROGRAMMING MODEL – Open source grid middleware packages – Globus Toolkit (GT4) Architecture, Configuration – Usage of Globus – Main components and Programming model – Introduction to Hadoop Framework – Mapreduce, Input splitting, map and reduce functions, specifying input and output parameters, configuring and running a job – Design of Hadoop file system, HDFS concepts, command line and java interface, dataflow of File read & File write. UNIT V SECURITY – Trust models for Grid security environment – Authentication and Authorization methods – Grid security infrastructure – Cloud Infrastructure security: network, host and application level – aspects of data security, provider data and its security, Identity and access management architecture, IAM practices in the cloud, SaaS, PaaS, IaaS availability in the cloud, Key privacy issues in the cloud. |
| cloud computing textbook by kai hwang: Analytics for the Internet of Things (IoT) Andrew Minteer, 2017-07-24 Break through the hype and learn how to extract actionable intelligence from the flood of IoT data About This Book Make better business decisions and acquire greater control of your IoT infrastructure Learn techniques to solve unique problems associated with IoT and examine and analyze data from your IoT devices Uncover the business potential generated by data from IoT devices and bring down business costs Who This Book Is For This book targets developers, IoT professionals, and those in the field of data science who are trying to solve business problems through IoT devices and would like to analyze IoT data. IoT enthusiasts, managers, and entrepreneurs who would like to make the most of IoT will find this equally useful. A prior knowledge of IoT would be helpful but is not necessary. Some prior programming experience would be useful What You Will Learn Overcome the challenges IoT data brings to analytics Understand the variety of transmission protocols for IoT along with their strengths and weaknesses Learn how data flows from the IoT device to the final data set Develop techniques to wring value from IoT data Apply geospatial analytics to IoT data Use machine learning as a predictive method on IoT data Implement best strategies to get the most from IoT analytics Master the economics of IoT analytics in order to optimize business value In Detail We start with the perplexing task of extracting value from huge amounts of barely intelligible data. The data takes a convoluted route just to be on the servers for analysis, but insights can emerge through visualization and statistical modeling techniques. You will learn to extract value from IoT big data using multiple analytic techniques. Next we review how IoT devices generate data and how the information travels over networks. You'll get to know strategies to collect and store the data to optimize the potential for analytics, and strategies to handle data quality concerns. Cloud resources are a great match for IoT analytics, so Amazon Web Services, Microsoft Azure, and PTC ThingWorx are reviewed in detail next. Geospatial analytics is then introduced as a way to leverage location information. Combining IoT data with environmental data is also discussed as a way to enhance predictive capability. We'll also review the economics of IoT analytics and you'll discover ways to optimize business value. By the end of the book, you'll know how to handle scale for both data storage and analytics, how Apache Spark can be leveraged to handle scalability, and how R and Python can be used for analytic modeling. Style and approach This book follows a step-by-step, practical approach to combine the power of analytics and IoT and help you get results quickly |
| cloud computing textbook by kai hwang: Dynamic Cloud Collaboration Platform Mohammad Mehedi Hassan, Eui-Nam Huh, 2012-09-14 Present trends in cloud providers (CPs) capabilities have given rise to the interest in federating or collaborating clouds, thus allowing providers to revel on an increased scale and reach more than that is achievable individually. Current research efforts in this context mainly focus on building supply chain collaboration (SCC) models, in which CPs leverage cloud services from other CPs for seamless provisioning. Nevertheless, in the near future, we can expect that hundreds of CPs will compete to offer services and thousands of users will also compete to receive the services to run their complex heterogeneous applications on a cloud computing environment. In this open federation scenario, existing collaboration models (i.e. SCC) are not applicable since they are designed for static environments where a-priori agreements among the parties are needed to establish the federation. To move beyond these shortcomings, Dynamic Cloud Collaboration Platform establishes the basis for developing dynamic, advanced and efficient collaborative cloud service solutions that are scalable, high performance, and cost effective. We term the technology for inter-connection and inter-operation of CPs in open cloud federation as Dynamic Cloud Collaboration (DCC), in which various CPs (small, medium, and large) of complementary service requirements will collaborate dynamically to gain economies of scale and enlargements of their capabilities to meet quality of service (QoS) requirements of consumers. In this context, this book addresses four key issues - when to collaborate (triggering circumstances), whom to collaborate with (suitable partners), how to collaborate (architectural model), and how to demonstrate collaboration applicability (simulation study). It also provides solutions, which are effective in real environments. |
| cloud computing textbook by kai hwang: Grid Resource Management Jarek Nabrzyski, Jennifer M. Schopf, Jan Weglarz, 2012-12-06 Grid Resource Management: State of the Art and Future Trends presents an overview of the state of the field and describes both the real experiences and the current research available today. Grid computing is a rapidly developing and changing field, involving the shared and coordinated use of dynamic, multi-institutional resources. Grid resource management is the process of identifying requirements, matching resources to applications, allocating those resources, and scheduling and monitoring Grid resources over time in order to run Grid applications as efficiently as possible. While Grids have become almost commonplace, the use of good Grid resource management tools is far from ubiquitous because of the many open issues of the field, including the multiple layers of schedulers, the lack of control over resources, the fact that resources are shared, and that users and administrators have conflicting performance goals. |
| cloud computing textbook by kai hwang: Workflow Scheduling on Computing Systems Kenli Li, Xiaoyong Tang, Jing Mei, Longxin Zhang, Wangdong Yang, Keqin Li, 2022-08-12 This book will serve as a guide in understanding workflow scheduling techniques on computing systems such as Cluster, Supercomputers, Grid computing, Cloud computing, Edge computing, Fog computing, and the practical realization of such methods. It offers a whole new perspective and holistic approach in understanding computing systems’ workflow scheduling. Expressing and exposing approaches for various process-centric cloud-based applications give a full coverage of most systems’ energy consumption, reliability, resource utilization, cost, and application stochastic computation. By combining theory with application and connecting mathematical concepts and models with their resource management targets, this book will be equally accessible to readers with both Computer Science and Engineering backgrounds. It will be of great interest to students and professionals alike in the field of computing system design, management, and application. This book will also be beneficial to the general audience and technology enthusiasts who want to expand their knowledge on computer structure. |
| cloud computing textbook by kai hwang: Federated Learning Qiang Yang, Lixin Fan, Han Yu, 2020-11-25 This book provides a comprehensive and self-contained introduction to federated learning, ranging from the basic knowledge and theories to various key applications. Privacy and incentive issues are the focus of this book. It is timely as federated learning is becoming popular after the release of the General Data Protection Regulation (GDPR). Since federated learning aims to enable a machine model to be collaboratively trained without each party exposing private data to others. This setting adheres to regulatory requirements of data privacy protection such as GDPR. This book contains three main parts. Firstly, it introduces different privacy-preserving methods for protecting a federated learning model against different types of attacks such as data leakage and/or data poisoning. Secondly, the book presents incentive mechanisms which aim to encourage individuals to participate in the federated learning ecosystems. Last but not least, this book also describes how federated learning can be applied in industry and business to address data silo and privacy-preserving problems. The book is intended for readers from both the academia and the industry, who would like to learn about federated learning, practice its implementation, and apply it in their own business. Readers are expected to have some basic understanding of linear algebra, calculus, and neural network. Additionally, domain knowledge in FinTech and marketing would be helpful.” |
| cloud computing textbook by kai hwang: Reinforcement Learning, second edition Richard S. Sutton, Andrew G. Barto, 2018-11-13 The significantly expanded and updated new edition of a widely used text on reinforcement learning, one of the most active research areas in artificial intelligence. Reinforcement learning, one of the most active research areas in artificial intelligence, is a computational approach to learning whereby an agent tries to maximize the total amount of reward it receives while interacting with a complex, uncertain environment. In Reinforcement Learning, Richard Sutton and Andrew Barto provide a clear and simple account of the field's key ideas and algorithms. This second edition has been significantly expanded and updated, presenting new topics and updating coverage of other topics. Like the first edition, this second edition focuses on core online learning algorithms, with the more mathematical material set off in shaded boxes. Part I covers as much of reinforcement learning as possible without going beyond the tabular case for which exact solutions can be found. Many algorithms presented in this part are new to the second edition, including UCB, Expected Sarsa, and Double Learning. Part II extends these ideas to function approximation, with new sections on such topics as artificial neural networks and the Fourier basis, and offers expanded treatment of off-policy learning and policy-gradient methods. Part III has new chapters on reinforcement learning's relationships to psychology and neuroscience, as well as an updated case-studies chapter including AlphaGo and AlphaGo Zero, Atari game playing, and IBM Watson's wagering strategy. The final chapter discusses the future societal impacts of reinforcement learning. |
| cloud computing textbook by kai hwang: 并行计算导论 , 2003 责任者译名:格拉马。 |
| cloud computing textbook by kai hwang: Energy-Efficient Distributed Computing Systems Albert Y. Zomaya, Young Choon Lee, 2012-07-26 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 |
| cloud computing textbook by kai hwang: Cloud Computing and Distributed Systems Kai Hwang, Jack Dongarra, Geoffrey C. Fox, 2018-02-01 Cloud Computing and Distributed Systems |
| cloud computing textbook by kai hwang: Cloud Computing A. Srinivasan, 2014 |
| cloud computing textbook by kai hwang: Cloud Security and Privacy Tim Mather, Subra Kumaraswamy, Shahed Latif, 2009-09-11 You may regard cloud computing as an ideal way for your company to control IT costs, but do you know how private and secure this service really is? Not many people do. With Cloud Security and Privacy, you'll learn what's at stake when you trust your data to the cloud, and what you can do to keep your virtual infrastructure and web applications secure. Ideal for IT staffers, information security and privacy practitioners, business managers, service providers, and investors alike, this book offers you sound advice from three well-known authorities in the tech security world. You'll learn detailed information on cloud computing security that-until now-has been sorely lacking. Review the current state of data security and storage in the cloud, including confidentiality, integrity, and availability Learn about the identity and access management (IAM) practice for authentication, authorization, and auditing of the users accessing cloud services Discover which security management frameworks and standards are relevant for the cloud Understand the privacy aspects you need to consider in the cloud, including how they compare with traditional computing models Learn the importance of audit and compliance functions within the cloud, and the various standards and frameworks to consider Examine security delivered as a service-a different facet of cloud security |
| cloud computing textbook by kai hwang: Cybersecurity and Privacy in Cyber Physical Systems Yassine Maleh, Mohammad Shojafar, Ashraf Darwish, Abdelkrim Haqiq, 2019-05-01 Cybersecurity and Privacy in Cyber-Physical Systems collects and reports on recent high-quality research that addresses different problems related to cybersecurity and privacy in cyber-physical systems (CPSs). It Presents high-quality contributions addressing related theoretical and practical aspects Improves the reader’s awareness of cybersecurity and privacy in CPSs Analyzes and presents the state of the art of CPSs, cybersecurity, and related technologies and methodologies Highlights and discusses recent developments and emerging trends in cybersecurity and privacy in CPSs Proposes new models, practical solutions, and technological advances related to cybersecurity and privacy in CPSs Discusses new cybersecurity and privacy models, prototypes, and protocols for CPSs This comprehensive book promotes high-quality research by bringing together researchers and experts in CPS security and privacy from around the world to share their knowledge of the different aspects of CPS security. Cybersecurity and Privacy in Cyber-Physical Systems is ideally suited for policymakers, industrial engineers, researchers, academics, and professionals seeking a thorough understanding of the principles of cybersecurity and privacy in CPSs. They will learn about promising solutions to these research problems and identify unresolved and challenging problems for their own research. Readers will also have an overview of CPS cybersecurity and privacy design. |
| cloud computing textbook by kai hwang: Grid Computing Security Anirban Chakrabarti, 2007-05-26 Based on research and industry experience, this book structures the issues pertaining to grid computing security into three main categories: architecture-related, infrastructure-related, and management-related issues. It discusses all three categories in detail, presents existing solutions, standards, and products, and pinpoints their shortcomings and open questions. Together with a brief introduction into grid computing in general and underlying security technologies, this book offers the first concise and detailed introduction to this important area, targeting professionals in the grid industry as well as students. |
| cloud computing textbook by kai hwang: Intelligent Systems Cornelius T. Leondes, 2002-08-29 Intelligent systems, or artificial intelligence technologies, are playing an increasing role in areas ranging from medicine to the major manufacturing industries to financial markets. The consequences of flawed artificial intelligence systems are equally wide ranging and can be seen, for example, in the programmed trading-driven stock market crash of October 19, 1987. Intelligent Systems: Technology and Applications, Six Volume Set connects theory with proven practical applications to provide broad, multidisciplinary coverage in a single resource. In these volumes, international experts present case-study examples of successful practical techniques and solutions for diverse applications ranging from robotic systems to speech and signal processing, database management, and manufacturing. |
| cloud computing textbook by kai hwang: OpenStack in Action Cody Bumgardner, 2016-03-15 Summary OpenStack in Action offers the real world use cases and step-by-step instructions you can take to develop your own cloud platform from from inception to deployment. This book guides you through the design of both the physical hardware cluster and the infrastructure services you'll need to create a custom cloud platform. Purchase of the print book includes a free eBook in PDF, Kindle, and ePub formats from Manning Publications. About the Technology OpenStack is an open source framework that lets you create a private or public cloud platform on your own physical servers. You build custom infrastructure, platform, and software services without the expense and vendor lock-in associated with proprietary cloud platforms like Amazon Web Services and Microsoft Azure. With an OpenStack private cloud, you can get increased security, more control, improved reliability, and lower costs. About the Book OpenStack in Action offers real-world use cases and step-by-step instructions on how to develop your own cloud platform. This book guides you through the design of both the physical hardware cluster and the infrastructure services you'll need. You'll learn how to select and set up virtual and physical servers, how to implement software-defined networking, and technical details of designing, deploying, and operating an OpenStack cloud in your enterprise. You'll also discover how to best tailor your OpenStack deployment for your environment. Finally, you'll learn how your cloud can offer user-facing software and infrastructure services. What's Inside Develop and deploy an enterprise private cloud Private cloud technologies from an IT perspective Organizational impact of self-service cloud computing About the Reader No prior knowledge of OpenStack or cloud development is assumed. About the Author Cody Bumgardner is the Chief Technology Architect at a large university where he is responsible for the architecture, deployment, and long-term strategy of OpenStack private clouds and other cloud computing initiatives. Table of Contents PART 1 GETTING STARTED Introducing OpenStack Taking an OpenStack test-drive Learning basic OpenStack operations Understanding private cloud building blocks PART 2 WALKING THROUGH A MANUAL DEPLOYMENT Walking through a Controller deployment Walking through a Networking deployment Walking through a Block Storage deployment Walking through a Compute deployment PART 3 BUILDING A PRODUCTION ENVIRONMENT Architecting your OpenStack Deploying Ceph Automated HA OpenStack deployment with Fuel Cloud orchestration using OpenStack |
| cloud computing textbook by kai hwang: Edge AI Xiaofei Wang, Yiwen Han, Victor C. M. Leung, Dusit Niyato, Xueqiang Yan, Xu Chen, 2020-08-31 As an important enabler for changing people’s lives, advances in artificial intelligence (AI)-based applications and services are on the rise, despite being hindered by efficiency and latency issues. By focusing on deep learning as the most representative technique of AI, this book provides a comprehensive overview of how AI services are being applied to the network edge near the data sources, and demonstrates how AI and edge computing can be mutually beneficial. To do so, it introduces and discusses: 1) edge intelligence and intelligent edge; and 2) their implementation methods and enabling technologies, namely AI training and inference in the customized edge computing framework. Gathering essential information previously scattered across the communication, networking, and AI areas, the book can help readers to understand the connections between key enabling technologies, e.g. a) AI applications in edge; b) AI inference in edge; c) AI training for edge; d) edge computing for AI; and e) using AI to optimize edge. After identifying these five aspects, which are essential for the fusion of edge computing and AI, it discusses current challenges and outlines future trends in achieving more pervasive and fine-grained intelligence with the aid of edge computing. |
| cloud computing textbook by kai hwang: Web Programming Chris Bates, 2006-05-26 This text teaches the essentials of working with the most important web technologies. From client development using HTML and Javascript, through to full server side applications written in ASP and Perl. |
| cloud computing textbook by kai hwang: Cyberinfrastructure Technologies and Applications Junwei Cao, 2009 Cyberinfrastructure was proposed in a report of the NSF Blue-Ribbon advisory panel in 2003. Cyberinfrastructure will provide a unified environment to access and manage cyber resources, e.g. supercomputers, data archives, software services, scientific instruments and virtual organisations. In this book, the authors review latest research and development and discuss new technologies and applications involved in building Cyberinfrastructure. The purpose of this book is to provide a detailed summary of early experiences, practices and lessons leaned in building Cyberinfrastructure from multiple perspectives: software development and maintenance, resource integration and sharing, cyber environment construction, operation and management, testing and troubleshooting, application enabling, security and QoS ensuring. Consequently, this book can serve as a valuable source of reference and indispensable reading for researchers, educators, engineers, graduate students, and practitioners in the field of design and implementation of Cyberinfrastructure systems. |
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Meet your business challenges head on with cloud computing services from Google, including data management, hybrid & multi-cloud, and AI & ML.
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Bersiaplah mengatasi tantangan bisnis Anda dengan layanan cloud computing dari Google, termasuk pengelolaan data, hybrid cloud & multi-cloud, serta AI & ML.
Cloud-Computing-Dienste - Google Cloud
Meistern Sie geschäftliche Herausforderungen mit Cloud-Computing-Diensten von Google wie Datenverwaltung, Hybrid- und Multi-Cloud sowie KI und ML.
Google Cloud Platform
Google Cloud Platform lets you build, deploy, and scale applications, websites, and services on the same infrastructure as Google.
Servizi di cloud computing | Google Cloud
Affronta le tue sfide aziendali con i servizi di cloud computing di Google, inclusi gestione dei dati, ambienti ibridi e multi-cloud, AI e machine learning.
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