article

Elastic stream processing in the Cloud

Authors:

Waldemar Hummer,

Benjamin Satzger,

Schahram DustdarAuthors Info & Claims

Wiley Interdisciplinary Reviews: Data Mining and Knowledge Discovery, Volume 3, Issue 5

Pages 333 - 345

https://doi.org/10.1002/widm.1100

Published: 01 September 2013 Publication History

Abstract

Stream processing is a computing paradigm that has emerged from the necessity of handling high volumes of data in real time. In contrast to traditional databases, stream-processing systems perform continuous queries and handle data on-the-fly. Today, a wide range of application areas relies on efficient pattern detection and queries over streams. The advent of Cloud computing fosters the development of elastic stream-processing platforms, which are able to dynamically adapt based on different cost-benefit trade-offs. This article provides an overview of the historical evolution and the key concepts of stream processing, with special focus on adaptivity and Cloud-based elasticity.

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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: 1-Jan-2024
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cover image Wiley Interdisciplinary Reviews: Data Mining and Knowledge Discovery

Wiley Interdisciplinary Reviews: Data Mining and Knowledge Discovery Volume 3, Issue 5

September 2013

47 pages

EISSN:1942-4795

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John Wiley & Sons, Inc.

United States

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Published: 01 September 2013

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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: 1-Jan-2024
https://dl.acm.org/doi/10.1109/COMST.2023.3330953
Gulisano VMedvet E(2024)Evolutionary Computation Meets Stream ProcessingApplications of Evolutionary Computation10.1007/978-3-031-56852-7_24(377-393)Online publication date: 3-Mar-2024
https://dl.acm.org/doi/10.1007/978-3-031-56852-7_24
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https://dl.acm.org/doi/10.1145/3355399
Röger HMayer R(2019)A Comprehensive Survey on Parallelization and Elasticity in Stream ProcessingACM Computing Surveys10.1145/330384952:2(1-37)Online publication date: 30-Apr-2019
https://dl.acm.org/doi/10.1145/3303849
Rodrigo ADayarathna MJayasena S(2019)Privacy Preserving Elastic Stream Processing with Clouds Using Homomorphic EncryptionDatabase Systems for Advanced Applications10.1007/978-3-030-18579-4_16(264-280)Online publication date: 22-Apr-2019
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Mencagli GDazzi PTonci NPierre GFerreira PShrira L(2018)SpinStreamsProceedings of the 19th International Middleware Conference10.1145/3274808.3274814(66-79)Online publication date: 26-Nov-2018
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Liu XDastjerdi ACalheiros RQu CBuyya R(2017)A Stepwise Auto-Profiling Method for Performance Optimization of Streaming ApplicationsACM Transactions on Autonomous and Adaptive Systems10.1145/313261812:4(1-33)Online publication date: 14-Nov-2017
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Ravindra SDayarathna MJayasena SBinder WCortellessa VKoziolek ASmirni EPoess M(2017)Latency Aware Elastic Switching-based Stream Processing Over Compressed Data StreamsProceedings of the 8th ACM/SPEC on International Conference on Performance Engineering10.1145/3030207.3030227(91-102)Online publication date: 17-Apr-2017
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Chen WPaik ILi Z(2017)Cost-Aware Streaming Workflow Allocation on Geo-Distributed Data CentersIEEE Transactions on Computers10.1109/TC.2016.259557966:2(256-271)Online publication date: 1-Feb-2017
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Cao DKang LZhan HMei H(2017)Towards application-level elasticity on shared clusterFrontiers of Computer Science: Selected Publications from Chinese Universities10.1007/s11704-016-5386-911:5(803-820)Online publication date: 1-Oct-2017
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