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Cloud-based data stream processing

Authors:

Leonardo Aniello,

Leonardo Querzoni,

Zbigniew JerzakAuthors Info & Claims

DEBS '14: Proceedings of the 8th ACM International Conference on Distributed Event-Based Systems

Pages 238 - 245

https://doi.org/10.1145/2611286.2611309

Published: 26 May 2014 Publication History

Abstract

In this tutorial we present the results of recent research about the cloud enablement of data streaming systems. We illustrate, based on both industrial as well as academic prototypes, new emerging uses cases and research trends. Specifically, we focus on novel approaches for (1) scalability and (2) fault tolerance in large scale distributed streaming systems. In general, new fault tolerance mechanisms strive to be more robust and at the same time introduce less overhead. Novel load balancing approaches focus on elastic scaling over hundreds of instances based on the data and query workload. Finally, we present open challenges for the next generation of cloud-based data stream processing engines.

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Cited By

Trivedi NPanchal S(2024)Optimization enabled elastic scaling in cloud based on predicted load for resource managementMultiagent and Grid Systems10.3233/MGS-23000319:4(289-311)Online publication date: 4-Mar-2024
https://doi.org/10.3233/MGS-230003
Volnes EPlagemann TGoebel V(2024)To Migrate or Not to Migrate: An Analysis of Operator Migration in Distributed Stream ProcessingIEEE Communications Surveys & Tutorials10.1109/COMST.2023.333095326:1(670-705)Online publication date: Sep-2025
https://doi.org/10.1109/COMST.2023.3330953
Russo Russo GCardellini VLo Presti F(2023)Hierarchical Auto-scaling Policies for Data Stream Processing on Heterogeneous ResourcesACM Transactions on Autonomous and Adaptive Systems10.1145/359743518:4(1-44)Online publication date: 14-Oct-2023
https://dl.acm.org/doi/10.1145/3597435
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Index Terms

Cloud-based data stream processing
1. Software and its engineering
  1. Software organization and properties
    1. Software system structures
      1. Distributed systems organizing principles
        Organizing principles for web applications

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Published In

cover image ACM Conferences

DEBS '14: Proceedings of the 8th ACM International Conference on Distributed Event-Based Systems

May 2014

371 pages

ISBN:9781450327374

DOI:10.1145/2611286

General Chairs:
Umesh Bellur
IIT Bombay, India
,
Ravi Kothari
IBM India Research Labs

Copyright © 2014 Owner/Author.

Permission to make digital or hard copies of part or all of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for third-party components of this work must be honored. For all other uses, contact the Owner/Author.

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Published: 26 May 2014

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DEBS '14

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DEBS '14: The 8th ACM International Conference on Distributed Event-Based Systems

May 26 - 29, 2014

Mumbai, India

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DEBS '14 Paper Acceptance Rate 16 of 174 submissions, 9%;

Overall Acceptance Rate 145 of 583 submissions, 25%

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Cited By

Trivedi NPanchal S(2024)Optimization enabled elastic scaling in cloud based on predicted load for resource managementMultiagent and Grid Systems10.3233/MGS-23000319:4(289-311)Online publication date: 4-Mar-2024
https://doi.org/10.3233/MGS-230003
Volnes EPlagemann TGoebel V(2024)To Migrate or Not to Migrate: An Analysis of Operator Migration in Distributed Stream ProcessingIEEE Communications Surveys & Tutorials10.1109/COMST.2023.333095326:1(670-705)Online publication date: Sep-2025
https://doi.org/10.1109/COMST.2023.3330953
Russo Russo GCardellini VLo Presti F(2023)Hierarchical Auto-scaling Policies for Data Stream Processing on Heterogeneous ResourcesACM Transactions on Autonomous and Adaptive Systems10.1145/359743518:4(1-44)Online publication date: 14-Oct-2023
https://dl.acm.org/doi/10.1145/3597435
Russo Russo GVieira MCardellini VDi Marco ATuma P(2023)Using Reinforcement Learning to Control Auto-Scaling of Distributed ApplicationsCompanion of the 2023 ACM/SPEC International Conference on Performance Engineering10.1145/3578245.3585427(137-138)Online publication date: 15-Apr-2023
https://dl.acm.org/doi/10.1145/3578245.3585427
Sun JGan WChao HYu PDing W(2023)Internet of Behaviors: A SurveyIEEE Internet of Things Journal10.1109/JIOT.2023.3247594(1-1)Online publication date: 2023
https://doi.org/10.1109/JIOT.2023.3247594
Fragkoulis MCarbone PKalavri VKatsifodimos A(2023)A survey on the evolution of stream processing systemsThe VLDB Journal10.1007/s00778-023-00819-833:2(507-541)Online publication date: 22-Nov-2023
https://doi.org/10.1007/s00778-023-00819-8
Tsigkanos CBersani MFrangoudis PDustdar S(2022)Edge-Based Runtime Verification for the Internet of ThingsIEEE Transactions on Services Computing10.1109/TSC.2021.307495615:5(2713-2727)Online publication date: 1-Sep-2022
https://doi.org/10.1109/TSC.2021.3074956
Ramprasad BMishra PThiessen MChen Hda Silva Veith AGabel MBalmau OChow Ade Lara E(2022)Shepherd: Seamless Stream Processing on the Edge2022 IEEE/ACM 7th Symposium on Edge Computing (SEC)10.1109/SEC54971.2022.00011(40-53)Online publication date: Dec-2022
https://doi.org/10.1109/SEC54971.2022.00011
Herodotou HOdysseos LChen YLu J(2022)Automatic Performance Tuning for Distributed Data Stream Processing Systems2022 IEEE 38th International Conference on Data Engineering (ICDE)10.1109/ICDE53745.2022.00296(3194-3197)Online publication date: May-2022
https://doi.org/10.1109/ICDE53745.2022.00296
Wang XZhang CFang JZhang RQian WZhou A(2022)A comprehensive study on fault tolerance in stream processing systemsFrontiers of Computer Science: Selected Publications from Chinese Universities10.1007/s11704-020-0248-x16:2Online publication date: 1-Apr-2022
https://dl.acm.org/doi/10.1007/s11704-020-0248-x
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