Communication Dans Un Congrès Année : 2021

A federated fog-cloud framework for data processing and orchestration: A Case Study in Smart Cities

Résumé

The fog computing paradigm has been proposed to alleviate the pressures on cloud platforms for data processing and enable computation-intensive and delay-sensitive applications in smart cities. However, state-of-the-art approaches mainly advocate either cloudor fog-based data processing solutions, and they also lack a common framework for programming over the fog-cloud continuum. In this paper, we propose a distributed, fog-cloud data processing and orchestration framework, which is capable of exploiting the semantics of both fog platforms and the Cloud. Our framework can create on-demand process engine data flow (PEDF) spanning multiple device layers with various resource constraints. This will considerably help the developers rapidly develop and deploy data processing applications over the fog-cloud continuum. Our proposed framework is validated in a real-world scenario-IoT data streaming analytics for the smart green wall in a smart city-which demonstrates efficient resource usage and latency reduction.
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Dates et versions

hal-04901341 , version 1 (24-01-2025)

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Citer

Dapeng Lan, Yu Liu, Amir Taherkordi, Frank Eliassen, Stéphane Delbruel, et al.. A federated fog-cloud framework for data processing and orchestration: A Case Study in Smart Cities. SAC '21: The 36th ACM/SIGAPP Symposium on Applied Computing, Mar 2021, Virtual Event Republic of Korea, South Korea. pp.729 - 736, ⟨10.1145/3412841.3444962⟩. ⟨hal-04901341⟩

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