Cloud Computing

  • Created: 2015-10-18
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Name:Cloud Computing
No.:S081203ZY003Semester:Spring
Hour:40Credit:2.0
Teacher:Xu, Jungang
Introduction:
 

Prerequisite: Computer Network, Algorithm, Programming, Database

Aims and Requirements: This is a major course for postgraduates from computer science and technology. The course involves the academic thoughts and science issues, key technologies and practices, related commercial form and service model, challenges and application prospects and so on. Through the classroom instruction and hands-on experiment, the students are expected to know: (1) The fundamentals of cloud computing, including resources conformity model and Massive data memory model and MapReduce programming model and service seal method. (2) Distributed file system and NoSQL databases. (3) Deployment and application of open source virtualized platform Xen. (4) Deployment, application and development of open source system Hadoop. (5) Application and development technologies of SaaS, including multi-renters, deployment, and extension. (6) Service model of S3 and Salesforce. After this course, the students can improve their capability of understanding, summary and analysis of cloud computing fundamentals, of cloud computing service development and open source application deployment practice. A firm specialized foundation can be built for the research and application in related field in the future.

Content:
 
Chapter 1 The Evolution of Computing Mainframe Computing. Cluster Computing. Parallel Computing. Distributed Computing.
Grid Computing. Cloud Computing.
Chapter 2 The Fundamentals of Cloud Computing Concepts of cloud computing (industrial world, academic circle, third party organization). Architecture and characteristic of cloud computing. Major science issues in cloud computing. Core idea of cloud computing: (1) Data centre resources integration model—resources virtualization; (2) Data centre mass data storage model—distributed file system and structurized NoSQL database; (3) Data centre programming model—MapReduce programming model; (4) Service encapsulation and commercial model--IaaS/PaaS/SaaS and effectiveness computation model.
Chapter 3 Resources Virtualization Technology Concepts of virtualization. Categories of virtualization. Major technologies of
virtualization. Deployment, installation and application of Xen.
Chapter 4 Distributed File System Concepts, characteristics and basic requirements of distributed file system. Differences from traditional shared file system. Fault tolerance and security of distributed file system. Categories of distributed file system. Massive data level distributed file system. Google File System. Hadoop File System. Moosefs.
Chapter 5 NoSQL Database Relational database bottleneck. Massive data storage method. NoSQL database model. BigTable, Hbase and experiments on them.
Chapter 6 Programming Model for Massive Data Processing Main programming model for now. Characteristics of massive data processing programming. Concepts and categories of parallel programming. MapReduce programming thoughts. MapReduce structure. MapReduce programming experiments in Hadoop(installation and deployment of Hadoop is involved).
Chapter 7 The methods of Service encapsulation Concepts of service. Technology of service encapsulation, Concepts and implementation technology of IaaS. Concepts and implementation technology of SaaS(deployment, multi-renters, extention and so on). SaaS pattern development cases. SaaS pattern software development experiments(text processing, search engine, machine learning and so on).
Chapter 8 Commercial Service Model of Cloud Computing Commercial model of cloud computing. Economics of cloud computing. Commercial Service Model of cloud computing—effectiveness computation model. Effectiveness computation model case(Amazon S3, EC2, Salesforce, Google Application Engine and so on).
Chapter 9 The Challenges that Cloud Computing is faced with and its Prospects Major points related to cloud computing development in academic circle and industrial world. Challenges that cloud computing is faced with. Prospects of cloud computing.
Material:
 

Xu Jungang. Cloud computing course materials. Graduate University of Chinese Academy of Sciences, 2011.

References:
 

[1] Google Code University, http://code.google.com/edu/parallel/index.html.

[2] Introduction to Distributed System Design. http://code.google.com/edu/parallel/dsd-tutorial.html.

[3] Introduction to Parallel Programming and MapReduce. http://code.google.com/edu/parallel/mapreduce-tutorial.html

[4] Tom White. Hadoop: The Definitive Guide. O'Reilly press, 2009.

[5] Jimmy Lin and Chris Dyer, Data-Intensive Text Processing with MapReduce, 2010.