Towards Safer Heuristics With Xplain
Name
3696348.3696884.pdf
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462.14 KB
Format
Adobe PDF
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Author(s)
Karimi, Pantea
Pirelli, Solal
Kakarla, Siva Kesava Reddy
Beckett, Ryan
Segarra, Santiago
Li, Beibin
Namyar, Pooria
Arzani, Behnaz
Date Issued
November 18, 2024
Publisher
ACM|The 23rd ACM Workshop on Hot Topics in Networks
Citation
Karimi, Pantea, Pirelli, Solal, Kakarla, Siva Kesava Reddy, Beckett, Ryan, Segarra, Santiago et al. 2024. "Towards Safer Heuristics With Xplain."
Version
Final published version
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 XPlain, 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.
Description
HOTNETS ’24, November 18–19, 2024, Irvine, CA, USA
MIT Department
Massachusetts Institute of Technology. Department of Electrical Engineering and Computer Science
Terms of Use
Article is made available in accordance with the publisher's policy and may be subject to US copyright law. Please refer to the publisher's site for terms of use.
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DOI of Published Version
https://doi.org/10.1145/3696348.3696884
