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Cloud architecture for plant phenotyping research

Abstract : Digital phenotyping is an emergent science mainly based on imagery techniques. The tremendous amount of data generated needs important cloud computing for their processing. The coupling of recent advance of distributed databases and cloud computing offers new possibilities of big data management and data sharing for the scientific research. In this paper, we present a solution combining a lambda architecture built around Apache Druid and a hosting platform leaning on Apache Mesos. Lambda architecture has already proved its performance and robustness. However, the capacity of ingesting and requesting of the database is essential and can constitute a bottleneck for the architecture, in particular, for in terms of availability and response time of data. We focused our experimentation on the response time of different databases to choose the most adapted for our phenotyping architecture. Apache Druid has shown its ability to respond to typical queries of phenotyping applications in times generally inferior to the second.
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https://hal.inrae.fr/hal-02968818
Contributor : Isabelle Nault <>
Submitted on : Friday, October 16, 2020 - 10:17:07 AM
Last modification on : Tuesday, October 20, 2020 - 9:34:02 PM

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Olivier Debauche, Sidi Ahmed Mahmoudi, Nicolas de Cock, Saïd Mahmoudi, Pierre Manneback, et al.. Cloud architecture for plant phenotyping research. Concurrency and Computation: Practice and Experience, Wiley, 2020, 32 (17), ⟨10.1002/cpe.5661⟩. ⟨hal-02968818⟩

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