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     CUTE: Instructional Laboratories for
             Cloud Computing Education

                                 
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Cloud computing has emerged as a key computing paradigm for business, research, and education. Compared to the fast development of cloud-based applications and technology, higher education on cloud computing is seriously lagging behind. Like most computer science curricula, to achieve effective education, learning principles of cloud computing must be grounded in experience. This calls for effective laboratory exercises. However, since cloud computing education is still embryonic, there are no publicly accessible comprehensive laboratory exercises nationwide.

 

Building upon our experience on cloud computing education in the past years, we propose a set of laboratories for cloud computing education referred to as the CUTE labs. The CUTE labs are designed to use publicly available free cloud resources and open source software with no special requirement on computing infrastructures, so that they can be easily adopted and adapted at low cost. Four types of laboratories will be developed: the platform exploration labs, the data intensive scalable computing labs, the cloud economics labs, and the security and privacy labs. These labs cover the major principles of cloud computing and provide opportunities for students to develop essential skills for cloud computing practice. The labs and the CUTE environment will be tested and evaluated in different educational settings. The PIs have extensive educational and research experience in cloud computing, distributed computing, data management, mobile computing, operating systems, and security and privacy.

 

PI: Keke Chen, Co-PI: Bin Wang, Prabhaker Mateti

 

Advisory Committee:

Calton Pu (College of Computing, Georgia Tech)
    Jimmy Lin (Department of Computer Science, University of Waterloo)

Collaborators:
    Yu Liang (Department of Computer Science, University of Tennessee at Chattanooga)

New Labs:

Introduction Lab

Infrastructure Exploration Labs:

* Simple AWS Lab
    * Advanced AWS+Hadoop Lab (Requires the preparation on Hadoop/MapReduce)

* AWS+Docker Lab

* Docker Lab

* Virtualization Lab

Big Data Labs:

    * Hadoop/MapReduce Lab

* Pig Programming Lab

* Cassandra Lab

* Spark Lab

* Big Relational Data Analysis with Pig and Spark Lab
Security and Privacy Labs:

    * Data Privacy Lab

    * Cloud Security Lab

Cloud Economics Labs:
    * Resource Monitoring and Provisioning for Web Apps   

    * MapReduce Cost Modeling and Resource Provisioning

 

Evaluations:

The access is restricted. Please email keke.chen@wright.edu if you need this information.

 

 

Associated courses:

   CEG4360/6360: Distributed Systems and Cloud Computing

   CEG7380: Cloud Computing
   CEG 4350/6350: OS Internals and Design

   CEG 2350: OS Concepts and Usage

   CEG 4410/6410: Mobile Computing
   CEG 4400/6400: Computer Network Security
   CEG 4420/6420: Host Computer Security