Enhancing communication network robustness under adaptive load-capacity model and multistate stochastic Markov cascading failure model

With advancements in communication technology, the increasing complexity of network structures has made cascading failures a significant threat to service continuity and data security. To address these issues, we propose an adaptive load-capacity model and a multistate stochastic Markov model. These models investigate the dynamic characteristics of cascading failures in communication networks while enhancing network robustness. Traditional models typically consider only normal and failed states of nodes, ignoring the existence of a sub-failed state. We introduce a range of node states, including normal, failed, and sub-failed states. This combination, along with the adaptive load-capacity model, enables the network to dynamically adjust node loads, thereby reducing the risks associated with single-point failures. We conducted experiments that included a comparative analysis of theoretical and simulated cascading failures, with numerical simulation results showing good agreement with theoretical analysis during the early stages of cascading failures. In our assessment of various models under cascading failure scenarios, we found that incorporating node multistate characteristics, alongside the adaptive load-capacity model, markedly enhances network robustness. Analysis of the giant component size and the number of failed nodes illustrates that our adaptive load-capacity model outperforms traditional topology and routing optimization methods due to its enhancement of robustness during failures. We also explored the impact of various attack modes and recovery strategies on network robustness and conducted a sensitivity analysis of the parameters. The experimental results demonstrate that our proposed models significantly improve network stability, reduce the occurrence of cascading failures, and enhance data transmission security. This work provides a solid foundation for developing effective failure prevention and recovery strategies.