Contents PART I: CLOUD, BIG DATA, AND COGNITIVE COMPUTING 1

Transcription

1 Preface xiii PART I: CLOUD, BIG DATA, AND COGNITIVE COMPUTING 1 1 Princi ples of Cloud Computing Systems Elastic Cloud Systems for Scalable Computing Enabling Technologies for Cloud Computing Evolution of Scalable Distributed/Parallel Computing Virtualized Resources in Cloud Systems Cloud Computing versus On- Premise Computing Cloud Architectures Compared with Distributed Systems Basic Cloud Platform Architectures Public, Private, Community, and Hybrid Clouds Physical Clusters versus Virtual Clusters Comparison with Other Parallel/Distributed Systems Ser vice Models, Ecosystems, and Scalability Analy sis Cloud Ser vice Models: IaaS, PaaS, and SaaS Scalability Laws in Evaluating Cloud Per for mance Cloud Ecosystem and User Environments Gartner Hype Cycle for Cloud Computing Interaction among SMACT Technologies Availability, Mobility, and Cluster Optimization Availability Analy sis of Cloud Server Clusters Fault Tolerance in Virtual Cluster Operations Queueing Model of Multiserver Clusters in Clouds Multiserver Cluster Optimization for Cloud Computing Conclusions 50 Homework Problems 50

2 vi Contents 2 Data Analytics, Internet of Things and Cognitive Computing Big Data Science and Application Challenges Data Science and Big Data Characteristics Gartner Hype Cyde for the Internet of Things Towards a Big Data Industry Big Data Applications: An Overview The Internet of Things and Cloud Interactions IoT Sensing and Platform Architecture IoT Value Chains and Development Road Map Stand- alone and Cloud- centric IoT Applications Smart City and Smart Community Development Data Collection, Mining, and Analytics on Clouds Data Quality Control and Repre sen ta tions Data Mining and Data Analytics Upgrading Data Analytics on Clouds Cloud Resources for Supporting Big Data Analytics Neuromorphic Hardware and Cognitive Computing Cognitive Computing and Neuromorphic Pro cessors SyNAPSE and Related Neurocomputer Proj ects at IBM Cambricom NPU at the Chinese Acad emy of Sciences Google s TPU and Related AI Programs Conclusions 106 Homework Problems 107 PART II: CLOUD ARCHITECTURE AND SERVICE PLATFORM DESIGN Virtual Machines, Docker Containers, and Server Clusters Virtualization in Cloud Computing Systems Basic Concept of Machine Virtualization Implementation Levels of Virtualization Resources Virtualization in Cluster or Cloud Systems Hypervisors for Creating Native Virtual Machines Virtual Machine Architecture Types Full Virtualization and Hosted Virtualization Paravirtualization with Guest OS Modification Comparison of Platform Virtualization Software Products and Toolkits Docker Engine and Application Containers Virtualization at Linux Kernel Level 132

3 vii 3.4 Docker Containers and Deployment Requirements Docker Containers Created with Linux Kernel Functions Docker Containers versus Virtual Machines Architectural Evolution from VMs to Containers and Unikernel Virtual Machine Management and Container Orchestration VM Management Solutions VM Migration for Disaster Recovery Docker Container Scheduling and Orchestration Eucalyptus, OpenStack, and VMware for Cloud Construction Eucalyptus for Virtual Clustering in Private Clouds OpenStack Software for Building Private or Public Clouds VMware Virtualization Support for Building Hybrid Clouds Conclusions 160 Homework Problems Cloud Architectures and Ser vice Platform Design Cloud Architecture and Infrastructure Design Public Clouds and Ser vice Offerings Business Models of Cloud Ser vices Converting Data Centers to Cloud Platforms Elastic Resources Provisioning Methods Dynamic Deployment of Virtual Clusters Virtual Cluster Deployment Proj ects Virtual Cluster Configuration Adaptation Virtualization Support for Data Center Clusters VMware vsphere 6: A Commercial Cloud Operating System Amazon AWS Cloud and Ser vice Offerings Three Cloud Architectures and Ser vices Convergence AWS EC2 Compute Engine and S3 Storage Cloud Other AWS Cloud Ser vice Offerings Google App Engine and Microsoft Azure Google App Engine and Compute Engine Google Hardware/Software Support for Machine Learning Services Microsoft Azure and Ser vice Offerings Salesforce, IBM SmartCloud, and Other Clouds Salesforce Clouds for SaaS Ser vices IBM SmartCloud, IoT, and Cognitive Proj ects Clouds at SGI, NASA, and CERN 218

4 viii Contents 4.6 Conclusions 223 Homework Problems Clouds for Mobile, IoT, Social Media, and Mashup Ser vices Wireless Internet and Mobile Cloud Computing Mobile Devices and Internet Edge Networks Wi-Fi, Bluetooth, and Wireless Sensor Networks Cloudlet Mesh for Mobile Cloud Computing Mobile Clouds and Colocation Clouds IoT Sensing and Interaction with Clouds Local and Global Positioning Systems Cloud- Based RAN for Building Mobile Networks IoT Interaction Frameworks with Clouds and Devices Cloud Computing in Social Media Applications Social Media Big-Data Industrial Applications Social Networks and API for Social Media Applications Social Graph Properties and Repre sen ta tions Social Graph Analy sis on Smart Clouds Multicloud Mashup Architecture and Ser vice Cloud Mashup Architecture for Agility and Scalability Multicloud Mashup Ser vice Architecture Skyline Discovery of Mashup Ser vices Dynamic Composition of Mashup Ser vices Conclusions 277 Homework Problems 278 PART III: PRINCI PLES OF MACHINE LEARNING AND ARTIFICIAL INTELLIGENCE MACHINES Machine Learning Algorithms and Model Fitting Taxonomy of Machine Learning Methods Categories of Machine Learning Algorithms Supervised Machine Learning Algorithms Unsupervised Machine Learning Algorithms Supervised Regression and Classification Methods Linear Regression for Prediction or Forecasting Decision Trees for Machine Learning Bayesian Classifier with Training Samples Support Vector Machines (SVM) Clustering and Dimensionality Reduction Methods 310

5 ix Cluster Analy sis and K- Means Clustering Dimensionality Reduction and Reinforcement Learning Principal Component Analy sis Semi- Supervised Learning Methods Model Development for Machine Learning Applications Per for mance Metrics and Model-Fitting Cases Methods to Reduce Model Over- Fitting Methods to Avoid Model Under- Fitting Machine Learning Model Se lection Options Conclusions 333 Homework Problems Intelligent Machines and Deep Learning Networks Artificial Intelligence and Smart Machine Development Analy sis of 2016 Gartner Hype Cycle on Smart Machines Google s Development of AI Products and Ser vices Cognitive Ser vices at IBM and Other Companies Deep Learning Chips at Intel, Nvidia, and CAS/ICT Augmented/Virtual Real ity and Blockchain Technology Augmented, Mediated, and Virtual Realities (AR, MR, VR) Virtual Real ity and Product Reviews Block Chaining for Securing Business Transactions Artificial Neural Networks for Deep Learning Deep Learning Mimics Human Cognitive Functions Evolution of ANNs and Reported Applications Mathematical Description of an Artificial Neuron Multilayer Artificial Neural Network Forward Propagation and Backward Propagation in ANN Taxonomy of Deep Learning Networks Classes and Types of Deep Learning Networks Convolutional Neural Networks Connectivity in Deep Neural Networks Recurrent Neural Networks (RNNs) Deep Learning of Other Brain Functions Restricted Boltzmann Machines Deep Belief Networks Deep Learning to Explore Other Brain Functions Conclusions 393 Homework Problems 393

6 x Contents PART IV: CLOUD PROGRAMMING AND PER FOR MANCE BOOSTERS Cloud Programming with Hadoop and Spark Scalable Parallel Computing Over Large Clusters Characteristics of Scalable Computing From MapReduce to Hadoop and Spark Application Software Libraries for Big Data Pro cessing Hadoop Programming with YARN and HDFS The MapReduce Compute Engine MapReduce for Parallel Matrix Multiplication Hadoop Architecture and Recent Extensions Hadoop Distributed File System Hadoop YARN for Resource Management Spark Core and Resilient Distributed Data Sets Spark Core for General- Purpose Applications Resilient Distributed Data Sets Spark Programming with RDDs for DAG Tasks Spark SQL and Streaming Programming Spark SQL with Structured Data Spark Streaming with Live Stream of Data Spark Streaming Application Examples Spark MLlib for Machine Learning and GraphX for Graph Pro cessing Spark MLlib Library for Machine Learning Some MLlib Application Examples Spark GraphX for Graph Pro cessing Some GraphX Programming Examples Conclusions 452 Homework Problems TensorFlow, Keras, DeepMind, and Graph Analytics TensorFlow for Neural Network Computing Key Concepts of TensorFlow Tensors, Variables, Feed, and Fetch Operations Distributed TensorFlow Execution Environment Execution Sessions in TensorFlow Programs TensorFlow System for Deep Learning Layered TensorFlow System Architecture TensorFlow Installation on Vari ous Host Machines TensorFlow Ecosystem for Distributed Resources Sharing TensorFlow for Handwritten Digit Recognition 484

7 xi TensorFlow Applications for Cognitive Ser vices Google s DeepMind and Other AI Programs Reinforcement Deep Learning Algorithm Interaction Between Policy Network and Value Network Reinforcement Learning in the AlphaGo Program DeepMind Health Proj ect in the United Kingdom Predictive Software, Keras, DIGITS, and Graph Libraries Predictive Software Libraries for Cognitive Applications Keras Library and DIGITS 5 for Deep learning Graph- Parallel Computations on Clouds Community Detection in Social networks Conclusions 518 Homework Problems Cloud Per for mance, Security, and Data Privacy Introduction What Are Cloud Per for mance and QoS? How Do You Secure Clouds and Protect Shared Data? Cloud Per for mance Metrics and Benchmarks Auto- Scaling, Scale- Out, and Scale- Up Strategies Cloud Performance Metrics Cloud Per for mance Models Expressed in Radar Charts Per for mance Analy sis of Cloud Benchmark Results Elastic Analy sis of Scalable Cloud Per for mance Scale- Out, Scale- Up, and Mixed Scaling Per for mance Relative Merits of Scaling Strategies Cloud Security and Data Privacy Protection Cloud Security and Privacy Issues Cloud Security Infrastructure Mobile Clouds and Security Threats Trust Management in Clouds and Datacenters Distributed Intrusion and Anomaly Detection Reputation- Based Trust Management in Clouds P2P Trust Overlay Network over Multiple Data Centers Conclusions 571 Homework Problems 571 Index 577