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Diamont: dynamic monitoring of uncertainty for distributed asynchronous programs.

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Název: Diamont: dynamic monitoring of uncertainty for distributed asynchronous programs.
Autoři: Fernando, Vimuth, Joshi, Keyur, Laurel, Jacob, Misailovic, Sasa
Zdroj: International Journal on Software Tools for Technology Transfer; Aug2023, Vol. 25 Issue 4, p521-539, 19p
Témata: MESSAGE passing (Computer science), DISTRIBUTED computing, MATHEMATICAL optimization, DYNAMICAL systems, NEUROLINGUISTICS, PRECISION farming
Abstrakt: 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. [ABSTRACT FROM AUTHOR]
Copyright of International Journal on Software Tools for Technology Transfer is the property of Springer Nature and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)
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  Data: 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. [ABSTRACT FROM AUTHOR]
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  Label:
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  Data: <i>Copyright of International Journal on Software Tools for Technology Transfer is the property of Springer Nature and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract.</i> (Copyright applies to all Abstracts.)
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        Value: 10.1007/s10009-023-00717-y
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        Text: English
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              Text: Aug2023
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