Latency Analysis of Cloud Infrastructure for Time-Critical IoT Use Cases
The time-critical Internet of Things (IoT) use cases such as driverless cars and robotic surgical arms need high bandwidth and low latency for real-time intelligent data processing and trained machine learning inference. Latency in real-time processing is influenced by many factors such as artific...
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| Main Authors: | , , , |
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| Format: | Proceeding |
| Language: | en |
| Published: |
IEEE
2022
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| Subjects: | |
| Online Access: | http://ir.unimas.my/id/eprint/41012/1/Latency%20Analysis.pdf http://ir.unimas.my/id/eprint/41012/ https://ieeexplore.ieee.org/document/9914601 |
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| Summary: | The time-critical Internet of Things (IoT) use cases such as driverless cars and robotic surgical arms need high
bandwidth and low latency for real-time intelligent data
processing and trained machine learning inference. Latency in real-time processing is influenced by many factors such as artificial intelligence (AI) computing algorithm, device
processing capabilities, the frameworks, and also the distance from the cloud infrastructure. However, the geographical distance between the data origin and data processing is one of the major factors contributing to the network latency for timecritical IoT use cases. In this paper, we analyzed the latency from a particular client point based on the live data generated by their cloud data centers. The experiments were done through the big three cloud vendors, which are Microsoft Azure, Amazon Web
Services (AWS), and Google Cloud Platform (GCP). As a result, a time-critical IoT low latency approach is proposed in this paper. |
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