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.
Author(s): Kai Hwang
Publisher: The MIT Press
Year: 2017
Language: English
Pages: 601
Contents (pg. v)
Preface (pg. xiii)
Part I: Cloud, Big Data, and Cognitive Computing (pg. 1)
1. Principles of Cloud Computing Systems (pg. 3)
1.1 Elastic Cloud Systems for Scalable Computing (pg. 3)
1.2 Cloud Architectures Compared with Distributed Systems (pg. 13)
1.3 Service Models, Ecosystems, and Scalability Analysis (pg. 25)
1.4 Availability, Mobility, and Cluster Optimization (pg. 40)
1.5 Conclusions (pg. 50)
Homework Problems (pg. 50)
2. Data Analytics, Internet of Things and Cognitive Computing (pg. 57)
2.1 Big Data Science and Application Challenges (pg. 57)
2.2 The Internet of Things and Cloud Interactions (pg. 68)
2.3 Data Collection, Mining, and Analytics on Clouds (pg. 82)
2.4 Neuromorphic Hardware and Cognitive Computing (pg. 97)
2.5 Conclusions (pg. 106)
Homework Problems (pg. 107)
Part II: Cloud Architecture and Service Platform Design (pg. 111)
3. Virtual Machines, Docker Containers, and Server Clusters (pg. 113)
3.1 Virtualization in Cloud Computing Systems (pg. 113)
3.2 Hypervisors for Creating Native Virtual Machines (pg. 121)
3.3 Docker Engine and Application Containers (pg. 132)
3.4 Docker Containers and Deployment Requirements (pg. 136)
3.5 Virtual Machine Management and Container Orchestration (pg. 144)
3.6 Eucalyptus, OpenStack, and VMware for Cloud Construction (pg. 153)
3.7 Conclusions (pg. 160)
Homework Problems (pg. 161)
4. Cloud Architectures and Service Platform Design (pg. 167)
4.1 Cloud Architecture and Infrastructure Design (pg. 167)
4.2 Dynamic Deployment of Virtual Clusters (pg. 180)
4.3 Amazon AWS Cloud and Service Offerings (pg. 188)
4.4 Google App Engine and Microsoft Azure (pg. 200)
4.5 Salesforce, IBM SmartCloud, and Other Clouds (pg. 212)
4.6 Conclusions (pg. 223)
Homework Problems (pg. 223)
5. Cloud for Mobile, IoT, Social Media and Mashup Services (pg. 229)
5.1 Wireless Internet and Mobile Cloud Computing (pg. 229)
5.2 IoT Sensing and Interaction with Clouds (pg. 240)
5.3 Cloud Computing in Social Media Applications (pg. 250)
5.4 Multicloud Mashup Architecture and Service (pg. 264)
5.5 Conclusions (pg. 277)
Homework Problems (pg. 278)
Part III: Principles of Machine Learning and Artificial Intelligence Machines (pg. 283)
6. Machine Learning Algorithms and Model Fitting (pg. 285)
6.1 Taxonomy of Machine Learning Methods (pg. 285)
6.2 Supervised Regression and Classification Methods (pg. 291)
6.3 Clustering and Dimensionality Reduction Methods (pg. 310)
6.4 Model Development for Machine Learning Applications (pg. 325)
6.5 Conclusions (pg. 333)
Homework Problems (pg. 334)
7. Intelligent Machines and Deep Learning Networks (pg. 341)
7.1 Artificial Intelligence and Smart Machine Development (pg. 341)
7.2 Augmented/Virtual Reality and Blockchain Technology (pg. 354)
7.3 Artificial Neural Networks for Deep Learning (pg. 360)
7.4 Taxonomy of Deep Learning Networks (pg. 376)
7.5 Deep Learning of Other Brain Functions (pg. 386)
7.6 Conclusions (pg. 393)
Homework Problems (pg. 393)
Part IV: Cloud Programming and Performance Boosters (pg. 401)
8. Cloud Programming with Hadoop and Spark (pg. 403)
8.1 Scalable Parallel Computing Over Large Clusters (pg. 403)
8.2 Hadoop Programming with YARN and HDFS (pg. 407)
8.3 Spark Core and Resilient Distributed Data Sets (pg. 426)
8.4 Spark SQL and Streaming Programming (pg. 435)
8.5 Spark MLlib for Machine Learning and GraphX for Graph Processing (pg. 442)
8.6 Conclusions (pg. 452)
Homework Problems (pg. 453)
9. TensorFlow, Keras, DeepMind, and Graph Analytics (pg. 463)
9.1 TensorFlow for Neural Network Computing (pg. 463)
9.2 TensorFlow System for Deep Learning (pg. 476)
9.3 Google’s DeepMind and Other AI Programs (pg. 494)
9.4 Predictive Software, Keras, DIGITS, and Graph Libraries (pg. 504)
9.5 Conclusions (pg. 518)
Homework Problems (pg. 518)
10. Cloud Performance, Security, and Data Privacy (pg. 521)
10.1 Introduction (pg. 521)
10.2 Cloud Performance Metrics and Benchmarks (pg. 525)
10.3 Performance Analysis of Cloud Benchmark Results (pg. 541)
10.4 Cloud Security and Data Privacy Protection (pg. 548)
10.5 Trust Management in Clouds and Datacenters (pg. 559)
10.6 Conclusions (pg. 571)
Homework Problems (pg. 571)
Index (pg. 577)
