Using clouds for MapReduce measurement assignments

Authors:
Ariel Rabkin;Charles Reiss;Randy Katz;David Patterson
Affiliations:
University of California Berkeley, UC Berkeley;University of California Berkeley, UC Berkeley;University of California Berkeley, UC Berkeley;University of California Berkeley, UC Berkeley
Venue:
ACM Transactions on Computing Education (TOCE)
Year:
2013

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Abstract

We describe our experiences teaching MapReduce in a large undergraduate lecture course using public cloud services and the standard Hadoop API. Using the standard API, students directly experienced the quality of industrial big-data tools. Using the cloud, every student could carry out scalability benchmarking assignments on realistic hardware, which would have been impossible otherwise. Over two semesters, over 500 students took our course. We believe this is the first large-scale demonstration that it is feasible to use pay-as-you-go billing in the cloud for a large undergraduate course. Modest instructor effort was sufficient to prevent students from overspending. Average per-pupil expenses in the Cloud were under $45. Students were excited by the assignment: 90% said they thought it should be retained in future course offerings.