research-article

JetStream: enabling high performance event streaming across cloud data-centers

Authors:

Alexandru Costan,

Gabriel AntoniuAuthors Info & Claims

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

Pages 23 - 34

https://doi.org/10.1145/2611286.2611298

Published: 26 May 2014 Publication History

Abstract

The easily-accessible computation power offered by cloud infrastructures coupled with the revolution of Big Data are expanding the scale and speed at which data analysis is performed. In their quest for finding the Value in the 3 Vs of Big Data, applications process larger data sets, within and across clouds. Enabling fast data transfers across geographically distributed sites becomes particularly important for applications which manage continuous streams of events in real time. Scientific applications (e.g. the Ocean Observatory Initiative or the ATLAS experiment) as well as commercial ones (e.g. Microsoft's Bing and Office 365 large-scale services) operate on tens of data-centers around the globe and follow similar patterns: they aggregate monitoring data, assess the QoS or run global data mining queries based on inter site event stream processing. In this paper, we propose a set of strategies for efficient transfers of events between cloud data-centers and we introduce JetStream: a prototype implementing these strategies as a high performance batch-based streaming middleware. JetStream is able to self-adapt to the streaming conditions by modeling and monitoring a set of context parameters. It further aggregates the available bandwidth by enabling multi-route streaming across cloud sites. The prototype was validated on tens of nodes from US and Europe data-centers of the Windows Azure cloud using synthetic benchmarks and with application code from the context of the Alice experiment at CERN. The results show an increase in transfer rate of 250 times over individual event streaming. Besides, introducing an adaptive transfer strategy brings an additional 25% gain. Finally, the transfer rate can further be tripled thanks to the use of multi-route streaming.

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Index Terms

JetStream: enabling high performance event streaming across cloud data-centers

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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 ACM.

Permission to make digital or hard copies of all or part 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 components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

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Published: 26 May 2014

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INRIA - Microsoft Research Center

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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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https://dl.acm.org/doi/10.1145/3458744.3474039
Vogel AGriebler DDanelutto MFernandes L(2021)Self‐adaptation on parallel stream processing: A systematic reviewConcurrency and Computation: Practice and Experience10.1002/cpe.675934:6Online publication date: 7-Dec-2021
https://doi.org/10.1002/cpe.6759
Zeng XGarg SBarika MZomaya AWang LVillari MChen DRanjan R(2020)SLA Management for Big Data Analytical Applications in CloudsACM Computing Surveys10.1145/338346453:3(1-40)Online publication date: 12-Jun-2020
https://dl.acm.org/doi/10.1145/3383464
Zamani ABalouek-Thomert DVillalobos JRodero IParashar M(2020)An edge-aware autonomic runtime for data streaming and in-transit processingFuture Generation Computer Systems10.1016/j.future.2020.03.037Online publication date: Apr-2020
https://doi.org/10.1016/j.future.2020.03.037
Nair BBhanu S(2019)Fog-Cloud Collaboration for Real-Time Streaming ApplicationsHandbook of Research on the IoT, Cloud Computing, and Wireless Network Optimization10.4018/978-1-5225-7335-7.ch007(128-147)Online publication date: 2019
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Balouek-Thomert DRenart EZamani ASimonet AParashar M(2019)Towards a computing continuum: Enabling edge-to-cloud integration for data-driven workflowsThe International Journal of High Performance Computing Applications10.1177/109434201987738333:6(1159-1174)Online publication date: 19-Sep-2019
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Zamani AAbdelBaky MBalouek‐Thomert DVillalobos JRodero IParashar M(2019)Submarine: A subscription‐based data streaming framework for integrating large facilities and advanced cyberinfrastructureConcurrency and Computation: Practice and Experience10.1002/cpe.525632:16Online publication date: 11-Apr-2019
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Cleveland SDooley RPerry DStubbs JFonner JJacobs GSanielevici S(2018)Building Science Gateway Infrastructure in the Middle of the Pacific and BeyondProceedings of the Practice and Experience on Advanced Research Computing: Seamless Creativity10.1145/3219104.3219151(1-8)Online publication date: 22-Jul-2018
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Dayarathna MPerera S(2018)Recent Advancements in Event ProcessingACM Computing Surveys10.1145/317043251:2(1-36)Online publication date: 13-Feb-2018
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Esmaili K(2017)Reflections on Almost Two Decades of Research into Stream ProcessingProceedings of the 11th ACM International Conference on Distributed and Event-based Systems10.1145/3093742.3095110(21-23)Online publication date: 8-Jun-2017
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