Cloud Computing for Machine Learning and Cognitive Applications

by Hwang

ISBN: 9780262341110 | 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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