Optimizing information support technology for network control: a probabilistic-time graph approach

Abstract

In modern telecommunications and computer networks, efficient and reliable information collection is essential for effective decision-making and control task resolution. Current methods, such as periodic data transmission, event-driven data collection, and on-demand requests, have distinct advantages and limitations. The object of the paper: The study focuses on developing a comprehensive model to optimize information collection processes in network environments. Subject of the paper: This paper investigates various information collection methods, including periodic data transmission, event-driven data collection, and on-demand requests, and evaluates their efficiency under different network conditions. This study proposes a flexible and accurate model that can optimize information support technologies for network control tasks. The key tasks include 1. Developing a probabilistic-time graph model to evaluate the efficiency of different information collection methods. 2. Analyzing model performance through mathematical relationships and simulations. 3. Comparing the proposed model with existing methodologies. Results. The proposed model demonstrated significant variations in the efficiency of the information collection methods. Periodic data transmission increased network load, while event-driven data collection was more responsive but could miss infrequent changes. On-demand requests balanced timely data needs with resource constraints but faced delays due to packet loss. The probabilistic time graph effectively captured these dynamics, providing a detailed understanding of the trade-offs. Conclusions. This study developed a flexible and accurate model for optimizing information support technologies during network control tasks. The model's adaptability to varying network conditions has significant practical implications for improving network efficiency and performance. Future research should explore the integration of machine learning techniques and extend the model to more complex network environments.