The great prosperity of big data systems such as Hadoop in recent years makes the benchmarking of these systems become crucial for both research and industry communities. The complexity, diversity, and rapid evolution of big data systems gives rise to various new challenges about how we design generators to produce data with the 4V properties (i.e. volume, velocity, variety and veracity), as well as implement application-specific but still comprehensive workloads. However, most of the existing big data benchmarks can be described as attempts to solve specific problems in benchmarking systems. This article investigates the state-of-the-art in benchmarking big data systems along with the future challenges to be addressed to realize a successful and efficient benchmark.
@article{arxiv.1506.01494,
title = {Benchmarking Big Data Systems: State-of-the-Art and Future Directions},
author = {Rui Han and Zhen Jia and Wanling Gao and Xinhui Tian and Lei Wang},
journal= {arXiv preprint arXiv:1506.01494},
year = {2015}
}
Comments
9 pages, 2 figures. arXiv admin note: substantial text overlap with arXiv:1402.5194