Mastering Cloud Computing

Author： Buyya, Rajkumar;Vecchiola, Christian;Selvi, S. Thamarai

Publishers： Elsevier Science

Publishing year： 2013

ISBN： 9780124095397

Subject： [TP393 计算机网络]

Language： ENG

Introduction

Mastering Cloud Computing is designed for undergraduate students learning to develop cloud computing applications. Tomorrow's applications won’t live on a single computer but will be deployed from and reside on a virtual server, accessible anywhere, any time. Tomorrow's application developers need to understand the requirements of building apps for these virtual systems, including concurrent programming, high-performance computing, and data-intensive systems.

The book introduces the principles of distributed and parallel computing underlying cloud architectures and specifically focuses on virtualization, thread programming, task programming, and map-reduce programming. There are examples demonstrating all of these and more, with exercises and labs throughout.

  • Explains how to make design choices and tradeoffs to consider when building applications to run in a virtual cloud environment
  • Real-world case studies include scientific, business, and energy-efficiency considerations

Chapter

  • Front Cover

  • Mastering Cloud Computing

  • Copyright page

  • Contents

  • Acknowledgments

  • Preface

  • The book at a glance

  • Benefits and readership

  • Directions for adoption: theory, labs, and projects

  • Supplemental materials

  • 1 Foundations

  • 1 Introduction

  • 1.1 Cloud computing at a glance

  • 1.1.1 The vision of cloud computing

  • 1.1.2 Defining a cloud

  • 1.1.3 A closer look

  • 1.1.4 The cloud computing reference model

  • 1.1.5 Characteristics and benefits

  • 1.1.6 Challenges ahead

  • 1.2 Historical developments

  • 1.2.1 Distributed systems

  • 1.2.2 Virtualization

  • 1.2.3 Web 2.0

  • 1.2.4 Service-oriented computing

  • 1.2.5 Utility-oriented computing

  • 1.3 Building cloud computing environments

  • 1.3.1 Application development

  • 1.3.2 Infrastructure and system development

  • 1.3.3 Computing platforms and technologies

  • 1.3.3.1 Amazon web services (AWS)

  • 1.3.3.2 Google AppEngine

  • 1.3.3.3 Microsoft Azure

  • 1.3.3.4 Hadoop

  • 1.3.3.5 Force.com and Salesforce.com

  • 1.3.3.6 Manjrasoft Aneka

  • Summary

  • Review questions

  • 2 Principles of Parallel and Distributed Computing

  • 2.1 Eras of computing

  • 2.2 Parallel vs. distributed computing

  • 2.3 Elements of parallel computing

  • 2.3.1 What is parallel processing?

  • 2.3.2 Hardware architectures for parallel processing

  • 2.3.2.1 Single-instruction, single-data (SISD) systems

  • 2.3.2.2 Single-instruction, multiple-data (SIMD) systems

  • 2.3.2.3 Multiple-instruction, single-data (MISD) systems

  • 2.3.2.4 Multiple-instruction, multiple-data (MIMD) systems

  • Shared memory MIMD machines

  • Distributed memory MIMD machines

  • 2.3.3 Approaches to parallel programming

  • 2.3.4 Levels of parallelism

  • 2.3.5 Laws of caution

  • 2.4 Elements of distributed computing

  • 2.4.1 General concepts and definitions

  • 2.4.2 Components of a distributed system

  • 2.4.3 Architectural styles for distributed computing

  • 2.4.3.1 Component and connectors

  • 2.4.3.2 Software architectural styles

  • Data centered architectures

  • Data-flow architectures

  • Virtual machine architectures

  • Call & return architectures

  • Architectural styles based on independent components

  • 2.4.3.3 System architectural styles

  • Client/server

  • Peer-to-peer

  • 2.4.4 Models for interprocess communication

  • 2.4.4.1 Message-based communication

  • 2.4.4.2 Models for message-based communication

  • Point-to-point message model

  • Publish-and-subscribe message model

  • Request-reply message model

  • 2.5 Technologies for distributed computing

  • 2.5.1 Remote procedure call

  • 2.5.2 Distributed object frameworks

  • 2.5.2.1 Object activation and lifetime

  • 2.5.2.2 Examples of distributed object frameworks

  • Common object request broker architecture (CORBA)

  • Distributed component object model (DCOM/COM+)

  • Java remote method invocation (RMI)

  • .NET remoting

  • 2.5.3 Service-oriented computing

  • 2.5.3.1 What is a service?

  • 2.5.3.2 Service-oriented architecture (SOA)

  • 2.5.3.3 Web services

  • 2.5.3.4 Service orientation and cloud computing

  • Summary

  • Review questions

  • 3 Virtualization

  • 3.1 Introduction

  • 3.2 Characteristics of virtualized environments

  • 3.2.1 Increased security

  • 3.2.2 Managed execution

  • 3.2.3 Portability

  • 3.3 Taxonomy of virtualization techniques

  • 3.3.1 Execution virtualization

  • 3.3.1.1 Machine reference model

  • 3.3.1.2 Hardware-level virtualization

  • Hypervisors

  • Hardware virtualization techniques

  • Operating system-level virtualization

  • 3.3.1.3 Programming language-level virtualization

  • 3.3.1.4 Application-level virtualization

  • 3.3.2 Other types of virtualization

  • 3.3.2.1 Storage virtualization

  • 3.3.2.2 Network virtualization

  • 3.3.2.3 Desktop virtualization

  • 3.3.2.4 Application server virtualization

  • 3.4 Virtualization and cloud computing

  • 3.5 Pros and cons of virtualization

  • 3.5.1 Advantages of virtualization

  • 3.5.2 The other side of the coin: disadvantages

  • 3.5.2.1 Performance degradation

  • 3.5.2.2 Inefficiency and degraded user experience

  • 3.5.2.3 Security holes and new threats

  • 3.6 Technology examples

  • 3.6.1 Xen: paravirtualization

  • 3.6.2 VMware: full virtualization

  • 3.6.2.1 Full virtualization and binary translation

  • 3.6.2.2 Virtualization solutions

  • End-user (desktop) virtualization

  • Server virtualization

  • Infrastructure virtualization and cloud computing solutions

  • 3.6.2.3 Observations

  • 3.6.3 Microsoft Hyper-V

  • 3.6.3.1 Architecture

  • Hypervisor

  • Enlightened I/O and synthetic devices

  • Parent partition

  • Child partitions

  • 3.6.3.2 Cloud computing and infrastructure management

  • 3.6.3.3 Observations

  • Summary

  • Review questions

  • 4 Cloud Computing Architecture

  • 4.1 Introduction

  • 4.2 The cloud reference model

  • 4.2.1 Architecture

  • 4.2.2 Infrastructure- and hardware-as-a-service

  • 4.2.3 Platform as a service

  • 4.2.4 Software as a service

  • 4.3 Types of clouds

  • 4.3.1 Public clouds

  • 4.3.2 Private clouds

  • 4.3.3 Hybrid clouds

  • 4.3.4 Community clouds

  • 4.4 Economics of the cloud

  • 4.5 Open challenges

  • 4.5.1 Cloud definition

  • 4.5.2 Cloud interoperability and standards

  • 4.5.3 Scalability and fault tolerance

  • 4.5.4 Security, trust, and privacy

  • 4.5.5 Organizational aspects

  • Summary

  • Review questions

  • 2 Cloud Application Programming and the Aneka Platform

  • 5 Aneka

  • 5.1 Framework overview

  • 5.2 Anatomy of the Aneka container

  • 5.2.1 From the ground up: the platform abstraction layer

  • 5.2.2 Fabric services

  • 5.2.2.1 Profiling and monitoring

  • 5.2.2.2 Resource management

  • 5.2.3 Foundation services

  • 5.2.3.1 Storage management

  • 5.2.3.2 Accounting, billing, and resource pricing

  • 5.2.3.3 Resource reservation

  • 5.2.4 Application services

  • 5.2.4.1 Scheduling

  • 5.2.4.2 Execution

  • 5.3 Building Aneka clouds

  • 5.3.1 Infrastructure organization

  • 5.3.2 Logical organization

  • 5.3.3 Private cloud deployment mode

  • 5.3.4 Public cloud deployment mode

  • 5.3.5 Hybrid cloud deployment mode

  • 5.4 Cloud programming and management

  • 5.4.1 Aneka SDK

  • 5.4.1.1 Application model

  • 5.4.1.2 Service model

  • 5.4.2 Management tools

  • 5.4.2.1 Infrastructure management

  • 5.4.2.2 Platform management

  • 5.4.2.3 Application management

  • Summary

  • Review questions

  • 6 Concurrent Computing

  • 6.1 Introducing parallelism for single-machine computation

  • 6.2 Programming applications with threads

  • 6.2.1 What is a thread?

  • 6.2.2 Thread APIs

  • 6.2.2.1 POSIX Threads

  • 6.2.2.2 Threading support in java and .NET

  • 6.2.3 Techniques for parallel computation with threads

  • 6.2.3.1 Domain decomposition

  • 6.2.3.2 Functional decomposition

  • 6.2.3.3 Computation vs. communication

  • 6.3 Multithreading with Aneka

  • 6.3.1 Introducing the thread programming model

  • 6.3.2 Aneka thread vs. common threads

  • 6.3.2.1 Interface compatibility

  • 6.3.2.2 Thread life cycle

  • 6.3.2.3 Thread synchronization

  • 6.3.2.4 Thread priorities

  • 6.3.2.5 Type serialization

  • 6.4 Programming applications with Aneka threads

  • 6.4.1 Aneka threads application model

  • 6.4.2 Domain decomposition: matrix multiplication

  • 6.4.3 Functional decomposition: Sine, Cosine, and Tangent

  • Summary

  • Review questions

  • 7 High-Throughput Computing

  • 7.1 Task computing

  • 7.1.1 Characterizing a task

  • 7.1.2 Computing categories

  • 7.1.2.1 High-performance computing

  • 7.1.2.2 High-throughput computing

  • 7.1.2.3 Many-task computing

  • 7.1.3 Frameworks for task computing

  • 7.2 Task-based application models

  • 7.2.1 Embarrassingly parallel applications

  • 7.2.2 Parameter sweep applications

  • 7.2.3 MPI applications

  • 7.2.4 Workflow applications with task dependencies

  • 7.2.4.1 What is a workflow?

  • 7.2.4.2 Workflow technologies

  • 7.3 Aneka task-based programming

  • 7.3.1 Task programming model

  • 7.3.2 Developing applications with the task model

  • 7.3.2.1 ITask and AnekaTask

  • 7.3.2.2 Controlling task execution

  • 7.3.2.3 File management

  • 7.3.2.4 Task libraries

  • 7.3.2.5 Web services integration

  • 7.3.3 Developing a parameter sweep application

  • 7.3.3.1 Object model

  • 7.3.3.2 Development and monitoring tools

  • 7.3.4 Managing workflows

  • Summary

  • Review questions

  • 8 Data-Intensive Computing

  • 8.1 What is data-intensive computing?

  • 8.1.1 Characterizing data-intensive computations

  • 8.1.2 Challenges ahead

  • 8.1.3 Historical perspective

  • 8.1.3.1 The early age: high-speed wide-area networking

  • 8.1.3.2 Data grids

  • 8.1.3.3 Data clouds and “Big Data”

  • 8.1.3.4 Databases and data-intensive computing

  • 8.2 Technologies for data-intensive computing

  • 8.2.1 Storage systems

  • 8.2.1.1 High-performance distributed file systems and storage clouds

  • 8.2.1.2 NoSQL systems

  • 8.2.2 Programming platforms

  • 8.2.2.1 The MapReduce programming model

  • 8.2.2.2 Variations and extensions of MapReduce

  • 8.2.2.3 Alternatives to MapReduce

  • 8.3 Aneka MapReduce programming

  • 8.3.1 Introducing the MapReduce programming model

  • 8.3.1.1 Programming abstractions

  • 8.3.1.2 Runtime support

  • 8.3.1.3 Distributed file system support

  • 8.3.2 Example application

  • 8.3.2.1 Parsing Aneka logs

  • 8.3.2.2 Mapper design and implementation

  • 8.3.2.3 Reducer design and implementation

  • 8.3.2.4 Driver program

  • 8.3.2.5 Running the application

  • Summary

  • Review questions

  • 3 Industrial Platforms and New Developments

  • 9 Cloud Platforms in Industry

  • 9.1 Amazon web services

  • 9.1.1 Compute services

  • 9.1.1.1 Amazon machine images

  • 9.1.1.2 EC2 instances

  • 9.1.1.3 EC2 environment

  • 9.1.1.4 Advanced compute services

  • AWS CloudFormation

  • AWS elastic beanstalk

  • Amazon elastic MapReduce

  • 9.1.2 Storage services

  • 9.1.2.1 S3 key concepts

  • Resource naming

  • Buckets

  • Objects and metadata

  • Access control and security

  • Advanced features

  • 9.1.2.2 Amazon elastic block store

  • 9.1.2.3 Amazon ElastiCache

  • 9.1.2.4 Structured storage solutions

  • Preconfigured EC2 AMIs

  • Amazon RDS

  • Amazon SimpleDB

  • 9.1.2.5 Amazon CloudFront

  • 9.1.3 Communication services

  • 9.1.3.1 Virtual networking

  • 9.1.3.2 Messaging

  • 9.1.4 Additional services

  • 9.1.5 Summary

  • 9.2 Google AppEngine

  • 9.2.1 Architecture and core concepts

  • 9.2.1.1 Infrastructure

  • 9.2.1.2 Runtime environment

  • Sandboxing

  • Supported runtimes

  • 9.2.1.3 Storage

  • Static file servers

  • DataStore

  • 9.2.1.4 Application services

  • UrlFetch

  • MemCache

  • Mail and instant messaging

  • Account management

  • Image manipulation

  • 9.2.1.5 Compute services

  • Task queues

  • Cron jobs

  • 9.2.2 Application life cycle

  • 9.2.2.1 Application development and testing

  • Java SDK

  • Python SDK

  • 9.2.2.2 Application deployment and management

  • 9.2.3 Cost model

  • 9.2.4 Observations

  • 9.3 Microsoft Azure

  • 9.3.1 Azure core concepts

  • 9.3.1.1 Compute services

  • Web role

  • Worker role

  • Virtual machine role

  • 9.3.1.2 Storage services

  • Blobs

  • Azure drive

  • Tables

  • Queues

  • 9.3.1.3 Core infrastructure: AppFabric

  • Access control

  • Service bus

  • Azure cache

  • 9.3.1.4 Other services

  • Windows Azure virtual network

  • Windows Azure content delivery network

  • 9.3.2 SQL Azure

  • 9.3.3 Windows Azure platform appliance

  • 9.3.4 Observations

  • Summary

  • Review questions

  • 10 Cloud Applications

  • 10.1 Scientific applications

  • 10.1.1 Healthcare: ECG analysis in the cloud

  • 10.1.2 Biology: protein structure prediction

  • 10.1.3 Biology: gene expression data analysis for cancer diagnosis

  • 10.1.4 Geoscience: satellite image processing

  • 10.2 Business and consumer applications

  • 10.2.1 CRM and ERP

  • 10.2.1.1 Salesforce.com

  • 10.2.1.2 Microsoft dynamics CRM

  • 10.2.1.3 NetSuite

  • 10.2.2 Productivity

  • 10.2.2.1 Dropbox and iCloud

  • 10.2.2.2 Google docs

  • 10.2.2.3 Cloud desktops: EyeOS and XIOS/3

  • 10.2.3 Social networking

  • 10.2.3.1 Facebook

  • 10.2.4 Media applications

  • 10.2.4.1 Animoto

  • 10.2.4.2 Maya rendering with Aneka

  • 10.2.4.3 Video encoding on the cloud: Encoding.com

  • 10.2.5 Multiplayer online gaming

  • Summary

  • Review questions

  • 11 Advanced Topics in Cloud Computing

  • 11.1 Energy efficiency in clouds

  • 11.1.1 Energy-efficient and green cloud computing architecture

  • 11.1.1.1 Energy-aware dynamic resource allocation

  • 11.1.1.2 InterClouds and integrated allocation of resources

  • 11.2 Market-based management of clouds

  • 11.2.1 Market-oriented cloud computing

  • 11.2.2 A reference model for MOCC

  • 11.2.2.1 A global view of market-oriented cloud computing

  • 11.2.2.2 Market-oriented architecture for datacenters

  • 11.2.3 Technologies and initiatives supporting MOCC

  • 11.2.3.1 Framework for trading computing utilities

  • 11.2.3.2 Industrial implementations

  • Flexible pricing models: amazon spot instances

  • Virtual market place: SpotCloud

  • Market directories: AppSpot, the cloud market

  • 11.2.3.3 A case study: the cloudbus toolkit

  • 11.2.4 Observations

  • 11.3 Federated clouds/InterCloud

  • 11.3.1 Characterization and definition

  • 11.3.2 Cloud federation stack

  • 11.3.2.1 Conceptual level

  • 11.3.2.2 Logical and operational level

  • 11.3.2.3 Infrastructural level

  • 11.3.3 Aspects of interest

  • 11.3.3.1 Standards

  • Open cloud manifesto

  • Distributed management task force

  • Open cloud computing interface

  • Cloud data management interface

  • Cloud security alliance

  • Other initiatives

  • 11.3.3.2 Security

  • Cloud federation security

  • 11.3.3.3 Legal issues

  • Privacy, security, and intellectual property related issues

  • Business and commerce-related issues

  • Jurisdictional and procedural issues

  • Implications in a cloud federation scenario

  • 11.3.4 Technologies for cloud federations

  • 11.3.4.1 Reservoir

  • 11.3.4.2 InterCloud

  • 11.3.5 Observations

  • 11.4 Third-party cloud services

  • 11.4.1 MetaCDN

  • 11.4.2 SpotCloud

  • Summary

  • Review questions

  • References

  • Index

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