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Scalability Evaluation of Big Data Processing Services in Clouds

  • Xin Zhou
  • Congfeng JiangEmail author
  • Yeliang Qiu
  • Tiantian Fan
  • Yumei Wang
  • Liangbin Zhang
  • Jian Wan
  • Weisong Shi
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 11459)

Abstract

Currently, many cloud providers deploy their big data processing systems as cloud services, which helps users conveniently manage and process their data in clouds. Among different service providers’ big data processing services, how to evaluate and compare their scalability is an interesting and challenging work. Most traditional benchmark tools focus on performance evaluation of big data processing systems, such as aggregated throughput and IOPS, but fail to conduct a quantitative analysis of their scalability. In this paper, we propose a measurement methodology to quantify the scalability of big data processing services, which makes the cloud services scalability comparable. We conduct a group of comparative experiments on AliCloud E-MapReduce and Baidu MRS, and collect their respective scalability characteristics under Hadoop and Spark workloads. The scalability characteristics observed in our work could help cloud users choose the best cloud service platform to set up an optimized big data processing system to achieve their specific goals more successfully.

Keywords

Big data Benchmark Scalability AliCloud Baidu cloud 

Notes

Acknowledgement

This work is supported by Natural Science Foundation of China (No. 61472109, No. 61572163 and No. 61472112) and Key Research and Development Program of Zhejiang Province (No. 2018C01098,2019C01059 and 2019C03134). This work is also supported in part by National Science Foundation (NSF) grant CNS-1205338 and CNS-1561216, and by the Introduction of Innovative R&D team program of Guangdong Province (No. 201001D0104726115). This work is supported by Alibaba Group through Alibaba Innovative Research (AIR) Program. This work is partially supported by Visiting Scholarship of Teachers’ Professional Development Program (No. FX2018050).

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Copyright information

© Springer Nature Switzerland AG 2019

Authors and Affiliations

  • Xin Zhou
    • 1
    • 2
  • Congfeng Jiang
    • 1
    • 2
    Email author
  • Yeliang Qiu
    • 1
    • 2
  • Tiantian Fan
    • 1
    • 2
  • Yumei Wang
    • 1
    • 2
  • Liangbin Zhang
    • 3
  • Jian Wan
    • 4
  • Weisong Shi
    • 5
  1. 1.Key Laboratory of Complex Systems Modeling and Simulation, Ministry of EducationHangzhou Dianzi UniversityHangzhouChina
  2. 2.School of Computer Science and TechnologyHangzhou Dianzi UniversityHangzhouChina
  3. 3.College of Big Data and Software EngineeringZhejiang Wanli UniversityNingboChina
  4. 4.School of Information and Electronic EngineeringZhejiang University of Science and TechnologyHangzhouChina
  5. 5.Department of Computer ScienceWayne State UniversityDetroitUSA

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