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CUTE Labs: Low-Cost Open-Source Instructional Laboratories for Cloud Computing Education

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2016 ASEE Annual Conference & Exposition


New Orleans, Louisiana

Publication Date

June 26, 2016

Start Date

June 26, 2016

End Date

August 28, 2016





Conference Session

NSF Grantees Poster Session II

Tagged Topic

NSF Grantees Poster Session

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Paper Authors


Keke Chen Wright State University

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Keke Chen is an associate professor in the Department of Computer Science and Engineering, a member of the Ohio Center of Excellence in Knowledge-Enabled Computing (the Kno.e.sis Center), at Wright State University. He directs the Data Intensive Analysis and Computing (DIAC) Lab at the Kno.e.sis Center. He earned his Ph.D. degree from Georgia Institute of Technology in 2006, his Master's degree from Zhejiang University in China in 1999, and his Bachelor's degree from Tongji University in China in 1996. All degrees are in Computer Science. His current research areas include cloud computing, secure data services and mining of outsourced data, the privacy issues in social computing, and visual exploration of big data. During 2006-2008, he was a senior research scientist at Yahoo! Labs, working on web search ranking, cross-domain ranking, and web-scale data mining. He owns three patents for his work in Yahoo!.

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Bin Wang Wright State University

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Prof. Bin Wang earned his Ph.D. from the Ohio State University in 2000. He joined the Wright State University in September 2000, where he is currently full professor of computer science and engineering. His research interests include optical networks, real-time computing, mobile and wireless networks, cognitive radio networks, trust and information security, and semantic web. He is a recipient of the US Department of Energy Career Award. His research has been supported by US Department of Energy, National Science Foundation, Air Force Office of Scientific Research, Air Force Research Laboratories, Ohio Supercomputer Center, and the State of Ohio.

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Prabhaker Mateti Wright State University

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Prabhaker Mateti,
Ph.D. in Computer Science, 1976, University of Illinois at

My research interests are in Internet security, distributed systems,
programming language design, technical aspects of software
engineering, and graph algorithms. My recent work is aimed at
strengthening the security of Operating Systems and the Internet via
auditing the existing code with the aid of mathematical verification
tools, and redesigning with security as the primary goal. I
regularly teach, among others, a course on Security that
was developed with funding from NSF.

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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. Thus, the education on cloud computing calls for effective laboratory exercises.

Building upon our experience with cloud computing education during the past years, we are developing a set of laboratories for cloud computing referred to as the Cloud compUTing Educational labs (i.e., the CUTE labs). The CUTE labs are designed to use publicly available free cloud resources and open source software with no particular requirement on computing infrastructures so that they can be readily adopted and adapted at low cost. Four types of laboratories are being 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.

CUTE labs have several appealing properties. (1) CUTE labs cover a broad spectrum of cloud computing concepts and principles, as well as necessary engineering skills. A general software/hardware framework is designed to support sustainable lab development. (2) CUTE labs are low-cost labs, and can be implemented with free cloud resources (e.g., Amazon Web Services in Education and Google App Engine) and open source software. They are replicable by even the most cash-strapped institutions. The low-cost design also ensures that the labs are self-sustainable after the grant ends. (3) CUTE labs are open source and allow instructors to adopt or revise to create their customized labs, which will foster a community of cloud computing educational labs.

So far we have developed 13 labs, among which 11 have been deployed and 6 have been evaluated. They have been deployed in the related courses at Wright State University during 2009-2015, such as Cloud Computing, Distributed System, Privacy-Aware Computing, Mobile Computing, and OS internals. During 2014-2015, we have evaluated 6 of 11. The preliminary results show that these labs have reached the initial design goals.

Chen, K., & Wang, B., & Mateti, P. (2016, June), CUTE Labs: Low-Cost Open-Source Instructional Laboratories for Cloud Computing Education Paper presented at 2016 ASEE Annual Conference & Exposition, New Orleans, Louisiana. 10.18260/p.26632

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