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Supporting applications in emerging domains like cyber-physical systems andhuman-in-the-loop scenarios typically requires adherence to strict end-to-enddelay guarantees. Contributions of many tandem processes unfolding layer bylayer within the wireless network result in violations of delay constraints,thereby severely degrading application performance. Meeting the application'sstringent requirements necessitates coordinated optimization of the end-to-enddelay by fine-tuning all contributing processes. To achieve this task, wedesigned and implemented EDAF, a framework to decompose packets' end-to-enddelays and determine each component's significance for 5G network. We showcaseEDAF on OpenAirInterface 5G uplink, modified to report timestamps across thedata plane. By applying the obtained insights, we optimized end-to-end uplinkdelay by eliminating segmentation and frame-alignment delays, decreasingaverage delay from 12ms to 4ms.

EDAF: An End-to-End Delay Analytics Framework for 5G-and-Beyond Networks