Cloud Computing for Machine Learning and Cognitive Applications
by Hwang
ISBN: 9780262364706 | Copyright 2017
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| 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) | |
Kai Hwang
Kai Hwang is a Professor of Electrical Engineering and Computer Science at the University of Southern California (USC). Cloud and Cognitive Computing is based on his Cloud Computing course.
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