Cloud-based data stream processing
Cloud-based data stream processing
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.
cloud-based data stream processing, fault tolerance, load balancing
238-245
Association for Computing Machinery
Heinze, Thomas
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Aniello, Leonardo
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Querzoni, Leonardo
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Jerzak, Zbigniew
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2014
Heinze, Thomas
cb743278-0f86-403d-b9ce-863d0bc354a1
Aniello, Leonardo
9846e2e4-1303-4b8b-9092-5d8e9bb514c3
Querzoni, Leonardo
c0eee656-74e7-419d-876c-3cad808683d6
Jerzak, Zbigniew
a03cdb3d-7057-4c51-8bd5-7e5fa7c165d3
Heinze, Thomas, Aniello, Leonardo, Querzoni, Leonardo and Jerzak, Zbigniew
(2014)
Cloud-based data stream processing.
In DEBS 2014 - Proceedings of the 8th ACM International Conference on Distributed Event-Based Systems.
Association for Computing Machinery.
.
(doi:10.1145/2611286.2611309).
Record type:
Conference or Workshop Item
(Paper)
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.
Text
Tutorial: Cloud-based Data Stream Processing
- Accepted Manuscript
More information
Accepted/In Press date: 1 February 2014
Published date: 2014
Additional Information:
Copyright:
Copyright 2014 Elsevier B.V., All rights reserved.
Venue - Dates:
8th ACM International Conference on Distributed Event-Based Systems, DEBS 2014, , Mumbai, India, 2014-05-26 - 2014-05-29
Keywords:
cloud-based data stream processing, fault tolerance, load balancing
Identifiers
Local EPrints ID: 419693
URI: http://eprints.soton.ac.uk/id/eprint/419693
PURE UUID: 6c343183-30b4-4d9f-b8ee-4b9d7c6b3bb4
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Date deposited: 19 Apr 2018 16:30
Last modified: 16 Mar 2024 04:32
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Contributors
Author:
Thomas Heinze
Author:
Leonardo Aniello
Author:
Leonardo Querzoni
Author:
Zbigniew Jerzak
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