Abstract
Distributed systems are widely used to share tasks in various systems which communicate to each other. one main reason to use these systems is their ability to keep each system’s privacy and share only the required information. The success of these systems relies on robust communications among their nodes. Advances in network and communication technologies have led to a more robust quality solution for distributed problems as these systems heavily rely on network robustness and stability. Despite this progress, the communication problems such as delay, loss, and noise still exist in many environments that have dramatically affected the quality of distributed problem solutions. In some recent studies, these issues have been explored partially; however, the need to investigate these issues’ impact specifically when combined seems necessary. This article studies the effect of message loss while there is a chance of distortion in the receiving messages. To have a better view of communication issues, both static and dynamic problems are tested. Three distributed algorithms, Distributed Stochastic Algorithm (DSA), Distributed Breakout Algorithm (DBA), and Max-Gain Message algorithm (MGM), are chosen to be tested in these environments, and their performance has been compared to each other. Test results show that all three algorithms are highly impacted by network instability, while DSA provides better results in general.
Recommended Citation
Dana, Saeid Samadi
(2024)
"Distributed Constraint Optimization Problem Solving in Unstable Environments,"
American Journal of Science & Engineering (AJSE): Vol. 2:
Iss.
1, Article 5.
Available at:
https://research.smartsociety.org/ajse/vol2/iss1/5