Abstract
Many problems that cloud operators solve are computationally expensive, and operators often use heuristic algorithms (that are faster and scale better than optimal) to solve them more efficiently. Heuristic analyzers enable operators to find when and by how much their heuristics underperform. However, these tools do not provide enough detail for operators to mitigate the heuristic’s impact in practice: they only discover a single input instance that causes the heuristic to underperform (and not the full set) and they do not explain why.
We propose $\mathcal{X}$Plain, a tool that extends these analyzers and helps operators understand when and why their heuristics underperform. We present promising initial results that show such an extension is viable.
BibTeX Citation
@inproceedings{10.1145/3696348.3696884,
author = {Karimi, Pantea and
Pirelli, Solal and
Kakarla, Siva Kesava Reddy and
Beckett, Ryan and
Segarra, Santiago and
Li, Beibin and
Namyar, Pooria and
Arzani, Behnaz},
title = {Towards Safer Heuristics With XPlain},
year = {2024},
isbn = {9798400712722},
publisher = {Association for Computing Machinery},
address = {New York, NY, USA},
url = {https://doi.org/10.1145/3696348.3696884},
doi = {10.1145/3696348.3696884},
booktitle = {Proceedings of the 23rd ACM Workshop on Hot Topics in Networks},
pages = {68–76},
numpages = {9},
keywords = {Domain-Specific Language, Explainable Analysis, Heuristic Analysis},
location = {Irvine, CA, USA},
series = {HotNets '24}
}