Architecting Time-Critical Big-Data (preprint)

Abstract : — Current infrastructures for developing big-data applications are able to process –via big-data analytics-huge amounts of data, using clusters of machines that collaborate to perform parallel computations. However, current infrastructures were not designed to work with the requirements of time-critical applications; they are more focused on general-purpose applications rather than time-critical ones. Addressing this issue from the perspective of the real-time systems community, this paper considers time-critical big-data. It deals with the definition of a time-critical big-data system from the point of view of requirements, analyzing the specific characteristics of some popular big-data applications. This analysis is complemented by the challenges stemmed from the infrastructures that support the applications, proposing an architecture and offering initial performance patterns that connect application costs with infrastructure performance.
Type de document :
Article dans une revue
IEEE Transactions on Big Data, 2016, 〈10.33/TBDATA.2017.2622712〉
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Contributeur : Pablo Basanta-Val <>
Soumis le : jeudi 3 novembre 2016 - 11:35:44
Dernière modification le : samedi 11 novembre 2017 - 19:04:02
Document(s) archivé(s) le : samedi 4 février 2017 - 12:46:32


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Pablo Basanta-Val, Neil Audsley, Andy Wellings, Ian Gray, Norberto Fernandez. Architecting Time-Critical Big-Data (preprint). IEEE Transactions on Big Data, 2016, 〈10.33/TBDATA.2017.2622712〉. 〈hal-01391133〉



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