Bahga, Arshdeep <br/><br/>
Cloud computing  : a hands-on approach 
Arsheep Bahga, Vijay Madisetti 
 - Kolkata  University press  c2014 
 - 454p.  P.B. 
<br/><br/> Table of Content<br/>Part I    Introduction and Concepts<br/><br/>1 Introduction to Cloud Computing <br/>1.1 Introduction <br/>1.1.1 Definition of Cloud Computing <br/>1.2 Characteristics of Cloud Computing <br/>1.3 Cloud Models <br/>1.3.1 Service Models <br/>1.3.2 Deployment Models <br/>1.4 Cloud Services Examples <br/>1.4.1 IaaS: Amazon EC2, Google Compute Engine, Azure VMs <br/>1.4.2 PaaS: Google App Engine <br/>1.4.3 SaaS: Salesforce <br/>1.5 Cloud-based Services & Applications <br/>1.5.1 Cloud Computing for Healthcare <br/>1.5.2 Cloud Computing for Energy Systems <br/>1.5.3 Cloud Computing for Transportation Systems <br/>1.5.4 Cloud Computing for Manufacturing Industry <br/>1.5.5 Cloud Computing for Government <br/>1.5.6 Cloud Computing for Education <br/>1.5.7 Cloud Computing for Mobile Communication<br/><br/>2 Cloud Concepts & Technologies <br/>2.1 Virtualization <br/>2.2 Load Balancing <br/>2.3 Scalability & Elasticity <br/>2.4 Deployment <br/>2.5 Replication <br/>2.6 Monitoring <br/>2.7 Software Defined Networking <br/>2.8 Network Function Virtualization <br/>2.9 MapReduce <br/>2.10  Identity and Access Management <br/>2.11  Service Level Agreements <br/>2.12  Billing <br/><br/>3 Cloud Services & Platforms <br/>3.1 Compute Services <br/>3.1.1 Amazon Elastic Compute Cloud <br/>3.1.2 Google Compute Engine <br/>3.1.3 Windows Azure Virtual Machines <br/>3.2 Storage Services <br/>3.2.1 Amazon Simple Storage Service <br/>3.2.2 Google Cloud Storage <br/>3.2.3 Windows Azure Storage <br/>3.3 Database Services <br/>3.3.1 Amazon Relational Data Store <br/>3.3.2 Amazon DynamoDB <br/>3.3.3 Google Cloud SQL <br/>3.3.4 Google Cloud Datastore <br/>3.3.5 Windows Azure SQL Database <br/>3.3.6 Windows Azure Table Service <br/>3.4 Application Services <br/>3.4.1 Application Runtimes & Frameworks <br/>3.4.2 Queuing Services <br/>3.4.3 Email Services <br/>3.4.4 Notification Services <br/>3.4.5 Media Services<br/>3.5 Content Delivery Services <br/>3.5.1 Amazon CloudFront <br/>3.5.2 Windows Azure Content Delivery Network <br/>3.6 Analytics Services <br/>3.6.1 Amazon Elastic MapReduce <br/>3.6.2 Google MapReduce Service <br/>3.6.3 Google BigQuery <br/>3.6.4 Windows Azure HDInsight <br/>3.7 Deployment & Management Services <br/>3.7.1 Amazon Elastic Beanstalk <br/>3.7.2 Amazon CloudFormation <br/>3.8 Identity & Access Management Services <br/>3.8.1 Amazon Identity & Access Management <br/>3.8.2 Windows Azure Active Directory <br/>3.9 Open Source Private Cloud Software <br/>3.9.1 CloudStack <br/>3.9.2 Eucalyptus <br/>3.9.3 OpenStack <br/><br/>4 Hadoop & MapReduce <br/>4.1 Apache Hadoop <br/>4.2 Hadoop MapReduce Job Execution <br/>4.2.1 NameNode <br/>4.2.2 Secondary NameNode <br/>4.2.3 JobTracker <br/>4.2.4 TaskTracker <br/>4.2.5 DataNode <br/>4.2.6 MapReduce Job Execution Workflow <br/>4.3 Hadoop Schedulers <br/>4.3.1 FIFO <br/>4.3.2 Fair Scheduler <br/>4.3.3 Capacity Scheduler <br/>4.4 Hadoop Cluster Setup <br/>4.4.1 Install Java <br/>4.4.2 Install Hadoop <br/>4.4.3 Networking <br/>4.4.4 Configure Hadoop <br/>4.4.5 Starting and Stopping Hadoop Cluster <br/><br/> Part II Developing for Cloud <br/><br/>5 Cloud Application Design <br/>5.1 Introduction <br/>5.2 Design Considerations for Cloud Applications <br/>5.2.1 Scalability <br/>5.2.2 Reliability & Availability <br/>5.2.3 Security <br/>5.2.4 Maintenance & Upgradation <br/>5.2.5 Performance <br/>5.3 Reference Architectures for Cloud Applications <br/>5.4 Cloud Application Design Methodologies <br/>5.4.1 Service Oriented Architecture <br/>5.4.2 Cloud Component Model <br/>5.4.3 IaaS, PaaS and SaaS Services for Cloud Applications <br/>5.4.4 Model View Controller <br/>5.4.5 RESTful Web Services <br/>5.5 Data Storage Approaches <br/>5.5.1 Relational (SQL) Approach <br/>5.5.2 Non-Relational (No-SQL) Approach <br/><br/>6 Python Basics <br/>6.1 Introduction <br/>6.2 Installing Python <br/>6.3 Python Data Types & Data Structures <br/>6.3.1 Numbers <br/>6.3.2 Strings <br/>6.3.3 Lists <br/>6.3.4 Tuples <br/>6.3.5 Dictionaries <br/>6.3.6 Type Conversions <br/>6.4 Control Flow <br/>6.4.1 if <br/>6.4.2 for <br/>6.4.3 while <br/>6.4.4 range <br/>6.4.5 break/continue <br/>6.4.6 pass <br/>6.5 Functions <br/>6.6 Modules <br/>6.7 Packages<br/>6.8 File Handling <br/>6.9 Date/Time Operations <br/>6.10 Classes  <br/><br/>7 Python for Cloud <br/>7.1 Python for Amazon Web Services <br/>7.1.1 Amazon EC2 <br/>7.1.2 Amazon AutoScaling <br/>7.1.3 Amazon S3 <br/>7.1.4 Amazon RDS <br/>7.1.5 Amazon DynamoDB <br/>7.1.6 Amazon SQS <br/>7.1.7 Amazon EMR <br/>7.2 Python for Google Cloud Platform <br/>7.2.1 Google Compute Engine <br/>7.2.2 Google Cloud Storage <br/>7.2.3 Google Cloud SQL <br/>7.2.4 Google BigQuery <br/>7.2.5 Google Cloud Datastore <br/>7.2.6 Google App Engine <br/>7.3 Python for Windows Azure <br/>7.3.1 Azure Cloud Service <br/>7.3.2 Azure Virtual Machines <br/>7.3.3 Azure Storage <br/>7.4 Python for MapReduce <br/>7.5 Python Packages of Interest <br/>7.5.1 JSON <br/>7.5.2 XML <br/>7.5.3 HTTPLib & URLLib <br/>7.5.4 SMTPLib <br/>7.5.5 NumPy <br/>7.5.6 Scikit-learn <br/>7.6 Python Web Application Framework - Django <br/>7.6.1 Django Architecture <br/>7.6.2 Starting Development with Django <br/>7.6.3 Django Case Study - Blogging App <br/>7.7 Designing a RESTful Web API <br/><br/>8 Cloud Application Development in Python <br/>8.1 Design Approaches <br/>8.1.1 Design Methodology for IaaS Service Model <br/>8.1.2 Design Methodology for PaaS Service Model <br/>8.2 Image Processing App <br/>8.3 Document Storage App <br/>8.4 MapReduce App <br/>8.5 Social Media Analytics App <br/><br/>Part III Advanced Topics <br/><br/>9 Big Data Analytics <br/>9.1 Introduction <br/>9.2 Clustering Big Data <br/>9.2.1 k-Means Clustering <br/>9.2.2 DBSCAN Clustering <br/>9.2.3 Parallelizing Clustering Algorithms Using MapReduce <br/>9.3 Classification of Big Data <br/>9.3.1 Naive Bayes <br/>9.3.2 Decision Trees <br/>9.3.3 Random Forest <br/>9.3.4 Support Vector Machine <br/>9.4 Recommendation Systems <br/><br/>10 Multimedia Cloud <br/>10.1 Introduction <br/>10.2 Case Study: Live Video Streaming App <br/>10.3 Streaming Protocols <br/>10.3.1 RTMP Streaming <br/>10.3.2 HTTP Live Streaming <br/>10.3.3 HTTP Dynamic Streaming <br/>10.4 Case Study: Video Transcoding App <br/><br/>11 Cloud Application Benchmarking & Tuning <br/>11.1 Introduction <br/>11.1.1 Trace Collection/Generation <br/>11.1.2 Workload Modeling <br/>11.1.3 Workload Specification <br/>11.1.4 Synthetic Workload Generation <br/>11.1.5 User Emulation vs. Aggregate Workloads <br/>11.2 Workload Characteristics <br/>11.3 Application Performance Metrics <br/>11.4 Design Considerations for a Benchmarking Methodology <br/>11.5 Benchmarking Tools <br/>11.5.1 Types of Tests <br/>11.6 Deployment Prototyping <br/>11.7 Load Testing & Bottleneck Detection Case Study <br/>11.8 Hadoop Benchmarking Case Study <br/><br/>12 Cloud Security <br/>12.1 Introduction <br/>12.2 CSA Cloud Security Architecture <br/>12.3 Authentication <br/>12.3.1 Single Sign-on (SSO) <br/>12.4 Authorization <br/>12.5 Identity & Access Management <br/>12.6 Data Security <br/>12.6.1 Securing Data at Rest <br/>12.6.2 Securing Data in Motion <br/>12.7 Key Management <br/>12.8 Auditing <br/><br/>13 Cloud for Industry, Healthcare & Education <br/>13.1 Cloud Computing for Healthcare <br/>13.2 Cloud Computing for Energy Systems <br/>13.3 Cloud Computing for Transportation Systems <br/>13.4 Cloud Computing for Manufacturing Industry <br/>13.5 Cloud Computing for Education <br/><br/>Appendix-A: Setting up Ubuntu VM  <br/>Appendix-B: Setting up Django  <br/><br/>Bibliography  <br/>Index   
<br/><br/><label>ISBN: </label>9788173719233  950.00 
<br/><br/><label>Subjects--Topical Terms: </label><br/>Cloud computing
<br/><br/><label>Dewey Class. No.: </label>R 004.6782 BAH(CLO)