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Integrating workload balancing and fault tolerance in distributed stream processing system

  • Junhua Fang
  • Pingfu Chao
  • Rong Zhang
  • Xiaofang Zhou
Article
  • 24 Downloads

Abstract

Distributed Stream Processing Engine (DSPE) is designed for processing continuous streams so as to achieve the real-time performance with low latency guaranteed. To satisfy such requirement, the availability and efficiency are the main concern of the DSPE system, which can be achieved by a proper design of the fault tolerance module and the workload balancing module, respectively. However, the inherent characteristics of data streams, including persistence, dynamic and unpredictability, pose great challenges in satisfying both properties. As far as we know, most of the state-of-the-art DSPE systems take either fault tolerance or workload balancing as its single optimization goal, which in turn receives a higher resource overhead or longer recovery time. In this paper, we combine the fault tolerance and workload balancing mechanisms in the DSPE to reduce the overall resource consumption while keeping the system interactive, high-throughput, scalable and highly available. Based on our data-level replication strategy, our method can handle the dynamic data skewness and node failure scenario: during the distribution fluctuation of the incoming stream, we rebalance the workload by selectively inactivate the data in high-load nodes and activate their replicas on low-load nodes to minimize the migration overhead within the stateful operator; when a fault occurs in the process, the system activates the replicas of the data affected to ensure the correctness while keeping the workload balanced. Extensive experiments on various join workloads on both benchmark data and real data show our superior performance compared with baseline systems.

Keywords

Real-time data processing Distributed systems Load Balancing High availability computing 

Notes

Acknowledgments

This work is partially supported by National Science Foundation of China under grant (No. 61802273, 61572194, 61772356, 61572335, and 61836007), Postdoctoral Research Foundation of China (2017M621813), Postdoctoral Science Foundation of Jiangsu Province (2018K029C), Natural science fund for colleges and universities in Jiangsu Province (18KJB520044), and Suzhou Science and Technology Development Program(SYG201803). This work is also supported by the Open Program of Neusoft Corporation(SKLSAOP1801) and Blockheaders Co. Ltd.

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Copyright information

© Springer Science+Business Media, LLC, part of Springer Nature 2019

Authors and Affiliations

  • Junhua Fang
    • 1
    • 4
  • Pingfu Chao
    • 2
  • Rong Zhang
    • 3
  • Xiaofang Zhou
    • 1
    • 2
  1. 1.Institute of Artificial Intelligence, School of Computer Science and TechnologySoochow UniversitySuzhouChina
  2. 2.School of Information Technology and Electrical EngineeringThe University of QueenslandBrisbaneAustralia
  3. 3.School of Data Science and EngineeringEast China Normal UniversityShanghaiChina
  4. 4.Neusoft CorporationShenyangChina

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