TY - JOUR
T1 - Diamont
T2 - dynamic monitoring of uncertainty for distributed asynchronous programs
AU - Fernando, Vimuth
AU - Joshi, Keyur
AU - Laurel, Jacob
AU - Misailovic, Sasa
N1 - Publisher Copyright:
© 2023, The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature.
PY - 2023/8
Y1 - 2023/8
N2 - 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.
AB - 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.
KW - Distributed Programs
KW - Runtime Analysis
KW - Uncertainty
UR - http://www.scopus.com/inward/record.url?scp=85175576994&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=85175576994&partnerID=8YFLogxK
U2 - 10.1007/s10009-023-00717-y
DO - 10.1007/s10009-023-00717-y
M3 - Article
AN - SCOPUS:85175576994
SN - 1433-2779
VL - 25
SP - 521
EP - 539
JO - International Journal on Software Tools for Technology Transfer
JF - International Journal on Software Tools for Technology Transfer
IS - 4
ER -