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Designing Parallel Data Processing for Enabling Large-Scale Sensor Applications

Milan Kabáč 1 Charles Consel 2, 1 Nic Volanschi 1 
1 Phoenix - Programming Language Technology For Communication Services
LaBRI - Laboratoire Bordelais de Recherche en Informatique, Inria Bordeaux - Sud-Ouest, EA4136 - Handicap et système nerveux :Action, communication, interaction: rétablissement de la fonction et de la participation [Bordeaux]
Abstract : Masses of sensors are being deployed at the scale of cities to manage parking spaces, transportation infrastructures to monitor traffic, and campuses of buildings to reduce energy consumption. These large-scale infrastructures become a reality for citizens via applications that orchestrate sensors to deliver high-value, innovative services. These applications critically rely on the processing of large amounts of data to analyze situations, inform users, and control devices. This paper proposes a design-driven approach to developing orchestrating applications for masses of sensors that integrates parallel processing of large amounts of data. Specifically, an application design exposes declarations that are used to generate a programming framework based on the MapReduce programming model. We have developed a prototype of our approach, using Apache Hadoop. We applied it to a case study and obtained significant speedups by parallelizing computations over twelve nodes. In doing so, we demonstrate that our design-driven approach allows to abstract over implementation details, while exposing architectural properties used to generate high-performance code for processing large datasets. Furthermore, we show that this high-performance support enables new, personalized services in a smart city. Finally, we discuss the expressiveness of our design language, identify some limitations, and present language extensions.
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Contributor : Eugene Volanschi Connect in order to contact the contributor
Submitted on : Friday, February 17, 2017 - 11:30:11 AM
Last modification on : Friday, July 8, 2022 - 10:08:25 AM
Long-term archiving on: : Thursday, May 18, 2017 - 2:23:57 PM


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Milan Kabáč, Charles Consel, Nic Volanschi. Designing Parallel Data Processing for Enabling Large-Scale Sensor Applications. Personal and Ubiquitous Computing, 2017, 21 (3), pp.457-473. ⟨10.1007/s00779-017-1009-1⟩. ⟨hal-01470281⟩



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