EDAF: An End-to-End Delay Analytics Framework for 5G-and-Beyond Networks
CoRR(2024)
摘要
Supporting applications in emerging domains like cyber-physical systems and
human-in-the-loop scenarios typically requires adherence to strict end-to-end
delay guarantees. Contributions of many tandem processes unfolding layer by
layer within the wireless network result in violations of delay constraints,
thereby severely degrading application performance. Meeting the application's
stringent requirements necessitates coordinated optimization of the end-to-end
delay by fine-tuning all contributing processes. To achieve this task, we
designed and implemented EDAF, a framework to decompose packets' end-to-end
delays and determine each component's significance for 5G network. We showcase
EDAF on OpenAirInterface 5G uplink, modified to report timestamps across the
data plane. By applying the obtained insights, we optimized end-to-end uplink
delay by eliminating segmentation and frame-alignment delays, decreasing
average delay from 12ms to 4ms.
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