Paper Info
Title
From TPC-C to Big Data Benchmarks: A Functional Workload Model
Abstract
Big data systems help organizations store, manipulate, and derive value from vast amounts of data. Relational database and MapReduce are the two most prominent technologies for such systems. Organizations use them to perform complex analysis on diverse and unconventional data types with fast growing data volumes. As more big data systems are deployed, the industry faces the challenge to develop representative benchmarks that can evaluate the capabilities of competing implementations. In this position paper, we argue for building future big data benchmarks using what we call a \"functional workload model\". This concept draws on combined experiences from standard benchmarks, exemplified by TPC-C. The functional workload model describes the functional goals that the system must achieve, the data access patterns, the load variations over time, and the computation required to achieve the functional goals. Abstracting functional workload models from empirical studies of MapReduce deployments represents the first step towards building truly representative big data benchmarks.
Year
DOI
Venue
2012
10.1007/978-3-642-53974-9_4
WBDB
Field
DocType
Volume
Data science,Relational database,Workload,System deployment,Computer science,Implementation,Data type,Application domain,Data access,Big data,Database
Conference
8163
ISSN
Citations 
PageRank 
0302-9743
12
0.74
References 
Authors
10
3
Name
Order
Citations
PageRank
Yanpei Chen191741.46
Francois Raab21626.02
Randy H. Katz3168193018.89