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Service Oriented Computing and Applications

, Volume 13, Issue 2, pp 169–183 | Cite as

Auto-scaling for real-time stream analytics on HPC cloud

  • Yingchao Cheng
  • Zhifeng HaoEmail author
  • Ruichu Cai
Original Research Paper
  • 53 Downloads

Abstract

There are very-high-volume streaming data in the cyber world today. With the popularization of 5G technology, the streaming Big Data grows larger. Moreover, it needs to be analyzed in real time. We propose a new strategy HPC2-ARS to enable streaming services on HPC platforms. This strategy includes a three-tier high-performance cloud computing (HPC2) platform and a novel autonomous resource-scheduling (ARS) framework. The HPC2 platform is our de facto base platform for research on streaming service. It has three components: Tianhe-2 high-performance computer, custom OpenStack cloud computing software, and Apache Storm stream data analytic system. Our ARS framework ensures real-time response on unpredictable and fluctuating stream, especially streaming Big Data in the 5G era. This strategy addresses an essential problem in the convergence of HPC Cloud, Big Data, and streaming service. Specifically, Our ARS framework provides theoretical and practical solutions for resource provisioning, placement, and scheduling optimization. Extensive experiments have validated the effectiveness of the proposed strategy.

Keywords

Distributed computing High-performance computing Resource management Service computing Stream Utility theory 

Notes

Acknowledgements

This work was supported in part by the Natural Science Foundation of China (NSFC)-Guangdong Joint Fund under Grant U1501254, in part by the NSFC under Grant 61876043 and Grant 61472089, in part by the China Scholarship Council under Grant 201608440336, in part by the Natural Science Foundation of Guangdong under Grant 2014A030306004 and Grant 2014A030308008, in part by the Guangdong High-level Personnel of Special Support Program under Grant 2015TQ01X140, in part by the Guangdong Provincial Key Laboratory of Cyber-Physical System under Grant 2016B030301008, and in part by the Pearl River S&T Nova Program of Guangzhou under Grant 201610010101.

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Copyright information

© Springer-Verlag London Ltd., part of Springer Nature 2019

Authors and Affiliations

  1. 1.School of ComputersGuangdong University of TechnologyGuangzhouChina
  2. 2.School of Mathematics and Big DataFoshan UniversityFoshanChina
  3. 3.Department of StatisticsTexas A&M UniversityCollege StationUSA

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