Using clouds for MapReduce measurement assignments
| Autor: | Randy H. Katz, Ariel Rabkin, Charles Reiss, David A. Patterson |
|---|---|
| Rok vydání: | 2013 |
| Předmět: |
General Computer Science
Multimedia business.industry Computer science Cost effectiveness media_common.quotation_subject Educational technology Cloud computing Benchmarking computer.software_genre Education Computer software Scalability ComputingMilieux_COMPUTERSANDEDUCATION Quality (business) business computer media_common |
| Zdroj: | ACM Transactions on Computing Education. 13:1-18 |
| ISSN: | 1946-6226 |
| DOI: | 10.1145/2414446.2414448 |
| Popis: | 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. |
| Databáze: | OpenAIRE |
| Externí odkaz: |
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