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Optimizing resource utilization in distributed computing systems for automotive applications

Abstract : One of the main challenges for the automobile industry in the digital age is to provide their customers with a reliable and ubiquitous level of connected services. Smart cars have been entering the market for a few years now to offer drivers and passengers safer, more comfortable, and entertaining journeys. All this by designing, behind the scenes, computer systems that perform well while conserving the use of resources.The performance of a Big Data architecture in the automotive industry relies on keeping up with the growing trend of connected vehicles and maintaining a high quality of service. The Cloud at Groupe PSA has a particular load on ensuring a real-time data processing service for all the brand's connected vehicles: with 200k connected vehicles sold each year, the infrastructure is continuously challenged.Therefore, this thesis mainly focuses on optimizing resource allocation while considering the specifics of continuous flow processing applications and proposing a modular and fine-tuned component architecture for automotive scenarios.First, we go over a fundamental and essential process in Stream Processing Engines, a resource allocation algorithm. The central challenge of deploying streaming applications is mapping the operator graph, representing the application logic, to the available physical resources to improve its performance. We have targeted this problem by showing that the approach based on inherent data parallelism does not necessarily lead to all applications' best performance.Second, we revisit the Big Data architecture and design an end-to-end architecture that meets today's demands of data-intensive applications. We report on CV's Big Data platform, particularly the one deployed by Groupe PSA. In particular, we present open-source technologies and products used in different platform components to collect, store, process, and, most importantly, exploit big data and highlight why the Hadoop system is no longer the de-facto solution of Big Data. We end with a detailed assessment of the architecture while justifying the choices made during design and implementation.
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Submitted on : Tuesday, June 8, 2021 - 8:50:14 AM
Last modification on : Wednesday, June 9, 2021 - 3:22:37 AM

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  • HAL Id : tel-03252900, version 1

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Anthony Nassar. Optimizing resource utilization in distributed computing systems for automotive applications. Embedded Systems. Université Bourgogne Franche-Comté, 2021. English. ⟨NNT : 2021UBFCD014⟩. ⟨tel-03252900⟩

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