| Abstract |
Consistency Violation Fault (cvf) is a type of fault where stale information causes a distributed computation to execute incorrect transitions. Since cvf s are common in practice, understanding cvf s is important in designing resilient distributed programs. Some properties of cvf s in a few case-study self-stabilizing distributed programs have been investigated in previous studies. However, more analyses are still needed to obtain a more comprehensive quantitative characterization of this class of faults.
In this study, we focus on factors that could influence the impact of cvf s, such as the location and timing of cvf s. We also extend our analysis to more case-study problems, such as distributed machine learning. Our preliminary results show that location has an important role in determining the impact of cvf. |
| Authors |
Amit Garu  , Arya Tanmay Gupta  , Duong Nguyen , Sandeep S. Kulkarni
|
| Journal Info |
Association for Computing Machinery | ICDCN '25: Proceedings of the 26th International Conference on Distributed Computing and Networking , pages: 274 - 275
|
| Publication Date |
1/2/2025 |
| ISSN |
|
Type |
article |
| Open Access |
closed
|
| DOI |
https://doi.org/10.1145/3700838.3703656 |
| Keywords |
Characterization (Score: 0.6638028)
|
