Dynamic data streaming for an appliance

Patiño Martínez, Marta and Azqueta Alzúaz, Ainhoa (2019). Dynamic data streaming for an appliance. In: "8th International Conference on Data Science, Technology and Applications (DATA 2019)", 26-28 Jul 2019, Praga, República Checa. ISBN 978-989-758-377-3. pp. 470-477. https://doi.org/10.5220/0008319204700477.


Title: Dynamic data streaming for an appliance
  • Patiño Martínez, Marta
  • Azqueta Alzúaz, Ainhoa
Item Type: Presentation at Congress or Conference (Article)
Event Title: 8th International Conference on Data Science, Technology and Applications (DATA 2019)
Event Dates: 26-28 Jul 2019
Event Location: Praga, República Checa
Title of Book: Proceedings of the 8th International Conference on Data Science, Technology and Applications (DATA 2019)
Date: 2019
ISBN: 978-989-758-377-3
Volume: 1
Freetext Keywords: Data stream processing; NUMA aware; Appliances
Faculty: E.T.S. de Ingenieros Informáticos (UPM)
Department: Lenguajes y Sistemas Informáticos e Ingeniería del Software
Creative Commons Licenses: Recognition - No derivative works - Non commercial

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Many applications require to analyse large amounts of continuous flows of data produced by different data sources before the data is stored. Data streaming engines emerged as a solution for processing data on the fly. At the same time, computer architectures have evolved to systems with several interconnected CPUs and Non Uniform Memory Access (NUMA), where the cost of accessing memory from a core depends on how CPUs are interconnected. In order to get better resource utilization and adaptiveness to the load dynamic migration of queries must be available in data streaming engines. Moreover, data streaming applications require high availability so that failures do not cause service interruption and losing data. This paper presents the dynamic migration and fault-tolerance capabilities of UPM-CEP, a data streaming engine designed to take advantage of NUMA architectures. The preliminary evaluation using Intel HiBench benchmark shows the effect of the query migration and fault-tolerance o n the system performance.

Funding Projects

Horizon 2020732051CloudDBApplianceBULL SASEuropean cloud in-memory database appliance with predictable performance for critical applications
Government of SpainTIN2016-80350UnspecifiedUniversidad Politécnica de MadridCLOUDDB: una base de datos ultraescalable, eficiente y altamente disponible
Madrid Regional GovernmentS2018/TCS-4499EDGEDATA-CMUnspecifiedEDGEDATA: una infraestructura para sistemas híbridos altamente descentralizados
Horizon 2020779747BigDataStackIBM ISRAEL - SCIENCE AND TECHNOLOGY LTDHigh-performance data-centric stack for big data applications and operations
Madrid Regional GovernmentS2013TIC2894Cloud4BigDataUnspecifiedUnspecified

More information

Item ID: 56631
DC Identifier: https://oa.upm.es/56631/
OAI Identifier: oai:oa.upm.es:56631
DOI: 10.5220/0008319204700477
Official URL: http://www.scitepress.org/DigitalLibrary/Link.aspx?doi=10.5220/0008319204700477
Deposited by: Memoria Investigacion
Deposited on: 22 Oct 2019 09:15
Last Modified: 22 Oct 2019 09:15
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