Measuring Traffic Dynamics at the Edge




traffic dynamics meter, edge computing, approximate computing


This work aims to measure the impact of approximate computing on a case study of traffic dynamics metering on a System-on-Chip edge computing device. Firstly, the study proposes a baseline implementation of the metering system in C++. To analyze the application in detail, study profiled the baseline using a built-in instrumented profiler, presenting the overall performance of each of its parts. During the hotspot analysis, some parts had optimization opportunities exploitable by multi-threading and approximate computing techniques, particularly frame skipping, which is inspired by the loop perforation approximate technique. The first optimization employed was multi-threading, which led to a 1.32x speedup on the application without introducing errors in the metrics. Then, the meter was optimized by using frame skipping. This work demonstrated that adaptatively modifying the number of frames skipped improved the error in the final metrics compared to keeping it fixed. In terms of performance, the frame skipping brought the overall speed up to 1.76x. Approximate computing, in particular, frame skipping, managed to contribute up to 25% of the overall speedup, managing to accelerate the meter from 8.7 frames per second to 15 fps in the most critical case in exchange for some numerical error on the final metrics.


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How to Cite

Measuring Traffic Dynamics at the Edge. (2022). Uniciencia, 36(1), 1-9.



Original scientific papers (evaluated by academic peers)

How to Cite

Measuring Traffic Dynamics at the Edge. (2022). Uniciencia, 36(1), 1-9.

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