Advanced search
1 file | 884.23 KB Add to list

SZTS: a novel big data transportation system Benchmark suite

Author
Organization
Abstract
Data analytics is at the core of the supply chain for both products and services in modern economies and societies. Big data workloads however are placing unprecedented demands on computing technologies, calling for a deep understanding and characterization of these emerging workloads. In this paper, we propose ShenZhen Transportation System (SZTS), a novel big data Hadoop benchmark suite comprised of real-life transportation analysis applications with real-life input data sets from Shenzhen in China. SZTS uniquely focuses on a specific and real-life application domain whereas other existing Hadoop benchmark suites, such as HiBench and CloudRank-D, consist of generic algorithms with synthetic inputs. We perform a cross-layer workload characterization at both the job and microarchitecture level, revealing unique characteristics of SZTS compared to existing Hadoop benchmarks as well as general-purpose multi-core PARSEC benchmarks. We also study the sensitivity of workload behavior with respect to input data size, and propose a methodology for identifying representative input data sets.
Keywords
Performance Measurement, Big Data, ShenZhen Transportation System (SZTS), MapReduce/Hadoop, Benchmarking

Downloads

  • (...).pdf
    • full text
    • |
    • UGent only
    • |
    • PDF
    • |
    • 884.23 KB

Citation

Please use this url to cite or link to this publication:

MLA
Xiong, Wen et al. “SZTS: a Novel Big Data Transportation System Benchmark Suite.” Proceedings of the International Conference on Parallel Processing. IEEE Computer Society, 2015. 819–828. Print.
APA
Xiong, W., Yu, Z., Eeckhout, L., Bei, Z., Zhang, F., & Xu, C.-Z. (2015). SZTS: a novel big data transportation system Benchmark suite. Proceedings of the International Conference on Parallel Processing (pp. 819–828). Presented at the 44th Annual International Conference on Parallel Processing Workshops (ICPPW), IEEE Computer Society.
Chicago author-date
Xiong, Wen, Zhibin Yu, Lieven Eeckhout, Zhengdong Bei, Fan Zhang, and Cheng-Zhong Xu. 2015. “SZTS: a Novel Big Data Transportation System Benchmark Suite.” In Proceedings of the International Conference on Parallel Processing, 819–828. IEEE Computer Society.
Chicago author-date (all authors)
Xiong, Wen, Zhibin Yu, Lieven Eeckhout, Zhengdong Bei, Fan Zhang, and Cheng-Zhong Xu. 2015. “SZTS: a Novel Big Data Transportation System Benchmark Suite.” In Proceedings of the International Conference on Parallel Processing, 819–828. IEEE Computer Society.
Vancouver
1.
Xiong W, Yu Z, Eeckhout L, Bei Z, Zhang F, Xu C-Z. SZTS: a novel big data transportation system Benchmark suite. Proceedings of the International Conference on Parallel Processing. IEEE Computer Society; 2015. p. 819–28.
IEEE
[1]
W. Xiong, Z. Yu, L. Eeckhout, Z. Bei, F. Zhang, and C.-Z. Xu, “SZTS: a novel big data transportation system Benchmark suite,” in Proceedings of the International Conference on Parallel Processing, Beijing, China, 2015, pp. 819–828.
@inproceedings{7023427,
  abstract     = {Data analytics is at the core of the supply chain for both products and services in modern economies and societies. Big data workloads however are placing unprecedented demands on computing technologies, calling for a deep understanding and characterization of these emerging workloads. 

In this paper, we propose ShenZhen Transportation System (SZTS), a novel big data Hadoop benchmark suite comprised of real-life transportation analysis applications with real-life input data sets from Shenzhen in China. SZTS uniquely focuses on a specific and real-life application domain whereas other existing Hadoop benchmark suites, such as HiBench and CloudRank-D, consist of generic algorithms with synthetic inputs. 

We perform a cross-layer workload characterization at both the job and microarchitecture level, revealing unique characteristics of SZTS compared to existing Hadoop benchmarks as well as general-purpose multi-core PARSEC benchmarks. We also study the sensitivity of workload behavior with respect to input data size, and propose a methodology for identifying representative input data sets.},
  author       = {Xiong, Wen and Yu, Zhibin and Eeckhout, Lieven and Bei, Zhengdong and Zhang, Fan and Xu, Cheng-Zhong},
  booktitle    = {Proceedings of the International Conference on Parallel Processing},
  isbn         = {978-1-4673-7588-7},
  issn         = {0190-3918},
  keywords     = {Performance Measurement,Big Data,ShenZhen Transportation System (SZTS),MapReduce/Hadoop,Benchmarking},
  language     = {eng},
  location     = {Beijing, China},
  pages        = {819--828},
  publisher    = {IEEE Computer Society},
  title        = {SZTS: a novel big data transportation system Benchmark suite},
  url          = {http://dx.doi.org/10.1109/ICPP.2015.91},
  year         = {2015},
}

Altmetric
View in Altmetric
Web of Science
Times cited: