Folo: Latency and Quality Optimized Task Allocation in Vehicular Fog Computing

Chao Zhu; Jin Tao; Giancarlo Pastor; Yu Xiao; Yusheng Ji; Quan Zhou; Yong Li; Antti Ylä-Jääski

doi:10.1109/jiot.2018.2875520

October 11, 2018IEEE Internet of Things JournalOpen Access

Folo: Latency and Quality Optimized Task Allocation in Vehicular Fog Computing

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Key Points

Design and evaluate Folo, an algorithmic framework for task allocation that balances transmission latency and output quality across vehicular fog computing networks.
Formulated an optimization model targeting delay minimization and service quality maximization across dynamic vehicular nodes.
Developed task distribution policies tailored for edge and 5G vehicular communication architectures.
Demonstrates improved trade-offs between processing latency and service quality compared to conventional scheduling baselines.
Mitigates bottleneck delays during intensive computational offloading in mobile vehicular environments.

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Implication

Computational framework demonstrates optimized task allocation in vehicular fog computing, suggesting improved latency and processing efficiency for connected vehicles.

Abstract

| openaire: EC/H2020/815191/EU//PriMO-5G

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Cite This Study

Zhu et al. (2018) studied this question.

synapsesocial.com/papers/69d9775a2a25b240b7a3c681 https://doi.org/10.1109/jiot.2018.2875520

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Folo: Latency and Quality Optimized Task Allocation in Vehicular Fog Computing

Key Points

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Abstract

Discussion

Cite This Study

Folo: Latency and Quality Optimized Task Allocation in Vehicular Fog Computing

Key Points

No abstract yet

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Implication

Abstract

Discussion

Cite This Study

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