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Diamont: dynamic monitoring of uncertainty for distributed asynchronous programs

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  • Special Issue: RV 2021
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Abstract

Many application domains including graph analytics, the Internet-of-Things, precision agriculture, and media processing operate on noisy data and/or produce approximate results. These applications can distribute computation across multiple (often resource-constrained) processing units. Analyzing the reliability and accuracy of such applications is challenging, since most existing techniques operate on specific fixed-error models, check for individual properties, or can only be applied to sequential programs.

We present Diamont, a system for dynamic monitoring of uncertainty properties in distributed programs. Diamont programs consist of distributed processes that communicate via asynchronous message passing. Diamont includes datatypes that dynamically monitor uncertainty in data and provides support for checking predicates over the monitored uncertainty at runtime. We also present a general methodology for verifying the soundness of the runtime system and optimizations using canonical sequentialization.

We implemented Diamont for a subset of the Go language and evaluated eight programs from precision agriculture, graph analytics, and media processing. We show that Diamont can prove important end-to-end properties of program outputs for significantly larger inputs compared to prior work, with modest execution time overhead: 3% on average (max 16.3%) for our main evaluation input set and 15% on average for 8x larger inputs.

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Acknowledgements

We would like to thank the reviewers of this version, as well as the previous version that appeared in RV 2021 [22], for their useful suggestions.

Funding

The research presented in this paper was supported in part by NSF Grants No. CCF-1846354, CCF-1956374, CCF-2028861, and CCF-2008883, USDA Grant No. NIFA-2021-67021-33449, and a gift from Meta Platforms, Inc.

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  1. University of Illinois Urbana-Champaign, Urbana-Champaign, USA

    Vimuth Fernando, Keyur Joshi, Jacob Laurel & Sasa Misailovic

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  1. Vimuth Fernando
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  2. Keyur Joshi
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  3. Jacob Laurel
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  4. Sasa Misailovic
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Correspondence to Vimuth Fernando.

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Fernando, V., Joshi, K., Laurel, J. et al. Diamont: dynamic monitoring of uncertainty for distributed asynchronous programs. Int J Softw Tools Technol Transfer 25, 521–539 (2023). https://doi.org/10.1007/s10009-023-00717-y

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