2016 IEEE International Symposium on Performance Analysis of Systems and Software (ISPASS)
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Abstract

The previous major efforts on big data benchmark either propose a large amount of workloads (e.g. a recent comprehensive big data benchmark suite—BigDataBench [4]), which impose cognitive difficulty on workload characterization and serious benchmarking cost; or only select a few workloads according to so-called popularity[1], which lead to partial or biased observations.
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