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Exploring the Effect of Noisy Environment on Distributed Problem Solving

Despite the enormous advances in network and Communications technologies, many distributed systems face some issues such as noise in the communication network. The communication issues can question the robustness and trustworthiness of such systems. In addition, distributed problem solving has been of tremendous interest to the research community because of its benefits such as privacy control and distribution of tasks; however, fewer works have been dedicated to addressing the communication challenges. Some of these works have studied the dramatic effects of issues such as message delay and loss in distributed systems, but more studies need to be done. In this study, the effect of a noisy environment on distributed problem solving is investigated. The obtained results can help researchers designing more optimizing algorithms that can adapt themselves to environmental uncertainties. Three common distributed algorithms, Distributed Stochastic Algorithm (DSA), Distributed Breakout Algorithm (DBA), and Max-Gain Message algorithm (MGM), are selected to analyze the noise issue's effect on solving distributed constraint optimization problems. The performance of these three algorithms is compared in the presence of various degrees of noise. These algorithms are applied to solve both static and dynamic distributed constraint optimization problems to understand possible effects better. Results of empirical experiments show that in static problems, MGM and DBA produce better solutions, while in dynamic problems, DSA provides more promising solutions.