Buffy: A Formal Language-Based Framework for Network Performance Analysis

Despite recent advances in using formal methods for analyzing network performance, modeling network functionality for performance analysis remains challenging. Existing tools expect users to directly create the logical formulas corresponding to the network functionality of interest. This is often unintuitive, difficult to get right, and tightly coupled with the specific encoding and reasoning engine one chooses to use. Instead, we propose language abstractions that enable users to model network functionality and analysis tasks in an imperative solver-agnostic program, and a framework to transform them into a representation that can be analyzed by the appropriate solver. We outline our progress so far, demonstrating the potential of our approach through preliminary case studies and directions for future work.

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Publication Details

Published
2024-11-11
DOI
https://doi.org/10.1145/3696348.3696854
Primary Topic
Software System Performance and Reliability
Type
article
Field-Weighted Citation Impact
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Buffy: A Formal Language-Based Framework for Network Performance Analysis

Aarti Gupta, Mina Tahmasbi Arashloo, David Walker, J. Leon Zhao et al.
Software System Performance and Reliability
article

Buffy: A Formal Language-Based Framework for Network Performance Analysis

Aarti Gupta, Mina Tahmasbi Arashloo, David Walker, J. Leon Zhao, Amir Seyhani
article en

Abstract

Despite recent advances in using formal methods for analyzing network performance, modeling network functionality for performance analysis remains challenging. Existing tools expect users to directly create the logical formulas corresponding to the network functionality of interest. This is often unintuitive, difficult to get right, and tightly coupled with the specific encoding and reasoning engine one chooses to use. Instead, we propose language abstractions that enable users to model network functionality and analysis tasks in an imperative solver-agnostic program, and a framework to transform them into a representation that can be analyzed by the appropriate solver. We outline our progress so far, demonstrating the potential of our approach through preliminary case studies and directions for future work.

University of Waterloo (CA), Princeton University (US)
Openalex Percentile: Top 77%
Software System Performance and Reliability
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