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Summary Efficient information flow in the complex, often microscale simulation systems such as the social, artificial life, or traffic ones poses a significant challenge. It is difficult to implement a highly scalable system due to algorithmic problems, which significantly hamper the efficiency, especially in the case of maintaining a synchronized state in a parallelized, distributed environment. Our previous work presented a desynchronized method of information distribution in a simulation environment, inspired by the propagation of smell, and proved this method to be highly scalable. In this paper, we enhance and validate this method to ensure it does not invalidate the conclusions drawn from the simulation, enabling the development of efficient, scalable simulation systems. The prototype of the method presented here leverages the actor model for parallelization and cluster sharding mechanisms for cluster management, providing a comprehensive solution for large‐scale simulations, following realistic rules known from the nature. In order to validate the method of signal propagation modeling, three simulation models are created and tested. The validation is based on statistical analysis of metrics collected during the simulation execution. Statistical similarity of the results obtained from the distributed and nondistributed executions indicates that the distribution process does not impact the correctness of the simulation.

Modern, highly concurrent, and large-scale systems require new methods for design, testing, and monitoring. Their dynamics and scale require real-time tools that provide a holistic view of the whole system and the ability to show a more detailed view when needed. Such tools can help identify the causes of unwanted states, which is hardly possible with a static analysis or metrics-based approach. In this paper, a new tool for the analysis of distributed systems in Erlang is presented. It provides the real-time monitoring of system dynamics on different levels of abstraction. The tool has been used for analyzing a large-scale urban traffic simulation system running on a cluster of 20 computing nodes.

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