research-article

MorphStream: Adaptive Scheduling for Scalable Transactional Stream Processing on Multicores

Authors:

Volker MarklAuthors Info & Claims

Proceedings of the ACM on Management of Data, Volume 1, Issue 1

Article No.: 59, Pages 1 - 26

https://doi.org/10.1145/3588913

Published: 30 May 2023 Publication History

Abstract

Transactional stream processing engines (TSPEs) differ significantly in their designs, but all rely on non- adaptive scheduling strategies for processing concurrent state transactions. Subsequently, none exploit multicore parallelism to its full potential due to complex workload dependencies. This paper introduces MorphStream, which adopts a novel approach by decomposing scheduling strategies into three dimensions and then strives to make the right decision along each dimension, based on analyzing the decision trade-offs under varying workload characteristics. Compared to the state-of-the-art, MorphStream achieves up to 3.4 times higher throughput and 69.1% lower processing latency for handling real-world use cases with complex and dynamically changing workload dependencies.

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Cited By

Xiang SYang ZZhao JMao YZhang S(2024)MorphStream: Scalable Processing of Transactions over Streams2024 IEEE 40th International Conference on Data Engineering (ICDE)10.1109/ICDE60146.2024.00434(5485-5488)Online publication date: 13-May-2024
https://doi.org/10.1109/ICDE60146.2024.00434
Zhao JLiu HZhang SDuan ZLiao XJin HZhang Y(2024)Fast Parallel Recovery for Transactional Stream Processing on Multicores2024 IEEE 40th International Conference on Data Engineering (ICDE)10.1109/ICDE60146.2024.00122(1478-1491)Online publication date: 13-May-2024
https://doi.org/10.1109/ICDE60146.2024.00122
Mencagli GTorquati MGriebler DFais ADanelutto M(2024)General-purpose data stream processing on heterogeneous architectures with WindFlowJournal of Parallel and Distributed Computing10.1016/j.jpdc.2023.104782184:COnline publication date: 27-Feb-2024
https://dl.acm.org/doi/10.1016/j.jpdc.2023.104782
Show More Cited By

Index Terms

MorphStream: Adaptive Scheduling for Scalable Transactional Stream Processing on Multicores
1. Information systems
  1. Information systems applications
    1. Data mining
      1. Clustering

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cover image Proceedings of the ACM on Management of Data

Proceedings of the ACM on Management of Data Volume 1, Issue 1

PACMMOD

May 2023

2807 pages

EISSN:2836-6573

DOI:10.1145/3603164

Editor:
Divyakant Agrawal
UC Santa Barbara, United States

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Copyright © 2023 ACM.

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Publication History

Published: 30 May 2023

Published in PACMMOD Volume 1, Issue 1

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DFG Priority Program
German Federal Ministry of Education and Research (BMBF) under grants BIFOLD - Berlin Institute for the Foundations of Learning and Data
German Federal Ministry of Education and Research (BMBF) under grants BBDC - Berlin Big Data Center
National Research Foundation, Singapore and Infocomm Media Development Authority under its Future Communications Research & Development Programme
SUTD Start-up Research Grant
National Natural Science Foundation of China

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Cited By

Xiang SYang ZZhao JMao YZhang S(2024)MorphStream: Scalable Processing of Transactions over Streams2024 IEEE 40th International Conference on Data Engineering (ICDE)10.1109/ICDE60146.2024.00434(5485-5488)Online publication date: 13-May-2024
https://doi.org/10.1109/ICDE60146.2024.00434
Zhao JLiu HZhang SDuan ZLiao XJin HZhang Y(2024)Fast Parallel Recovery for Transactional Stream Processing on Multicores2024 IEEE 40th International Conference on Data Engineering (ICDE)10.1109/ICDE60146.2024.00122(1478-1491)Online publication date: 13-May-2024
https://doi.org/10.1109/ICDE60146.2024.00122
Mencagli GTorquati MGriebler DFais ADanelutto M(2024)General-purpose data stream processing on heterogeneous architectures with WindFlowJournal of Parallel and Distributed Computing10.1016/j.jpdc.2023.104782184:COnline publication date: 27-Feb-2024
https://dl.acm.org/doi/10.1016/j.jpdc.2023.104782
Zhang SSoto JMarkl V(2023)A survey on transactional stream processingThe VLDB Journal — The International Journal on Very Large Data Bases10.1007/s00778-023-00814-z33:2(451-479)Online publication date: 27-Sep-2023
https://dl.acm.org/doi/10.1007/s00778-023-00814-z

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