Abstract

We propose a performance analysis tool for learning-enabled systems that allows operators to uncover potential performance issues \emph{before} deploying DNNs in their systems. The tools that exist for this purpose require operators to faithfully model all components (a white-box approach) or do inefficient black-box local search. We propose a gray-box alternative, which eliminates the need to precisely model all the system’s components. Our approach is faster and finds substantially worse scenarios compared to prior work. We show that a state-of-the-art learning-enabled traffic engineering pipeline can underperform the optimal by $6\times$ — a much higher number compared to what the authors found.

BibTeX Citation

@inproceedings{10.1145/3696348.3696875,
author = {Namyar, Pooria and
          Schapira, Michael and
          Govindan, Ramesh and
          Segarra, Santiago and
          Beckett, Ryan and
          Kakarla, Siva Kesava Reddy and
          Arzani, Behnaz},
title = {End-to-End Performance Analysis of Learning-enabled Systems},
year = {2024},
isbn = {9798400712722},
publisher = {Association for Computing Machinery},
address = {New York, NY, USA},
url = {https://doi.org/10.1145/3696348.3696875},
doi = {10.1145/3696348.3696875},
booktitle = {Proceedings of the 23rd ACM Workshop on Hot Topics in Networks},
pages = {86–94},
numpages = {9},
keywords = {Machine Learning for Systems, Performance Analysis},
location = {Irvine, CA, USA},
series = {HotNets '24}
}