Failover planning for geographically redundant Multiprotocol Label Switching (MPLS) backbones must distinguish directional rate adequacy, packet deadlines, and interruption loss. This study develops a reproducible assessment combining G.729 traffic accounting, stochastic route/recovery trials, and independently verified finite buffer packet events. At 462 calls and 11.85 Mbit/s per direction, three-realization delivery by 100 ms averages 99.721% for independent phases and 90.818% for a 4 ms phase cluster. A matched comparison across 64 policy cells admits 30 using mean delay and blackout criteria, 19 using the full packet criterion, and eight using a sufficient all-phase bound. These counts persist at timely delivery targets of 98%, 99%, and 99.5% with the original blackout limit. Normalized analysis and five traffic levels from −20% to +20% identify when additional capacity, deadline headroom, or shorter recovery is required. A targeted 800-trace extension uses fifty realizations per cell and bounded one-sided release delays of 1–2 ms. The tested admission contrast persists, while shorter typical deadline miss runs can coexist with slightly worse aggregate timely delivery. The contribution is a verified comparison of conditional planning decisions and continuity consequences. OPNET illustrations provide configuration provenance; operational transfer requires calibrated traffic, service, and recovery inputs.

Dense Wavelength Division Multiplexing (DWDM) networks are pivotal in modern optical communications, facilitating high data capacity by transmitting multiple wavelengths along a single optical fiber. Ensuring optimal network performance and reliability requires close monitoring of crucial signal characteristics such as signal-to-noise ratio (SNR), chromatic dispersion, nonlinear effects, and modulation types. However, traditional monitoring approaches often demand sophisticated and costly equipment, limiting their scalability. This paper proposes an innovative machine learning (ML)-based solution for enhanced signal monitoring and anomaly detection within DWDM networks. Utilizing K-means clustering for data categorization and feature extraction, alongside Support Vector Machine (SVM) for effective signal classification and anomaly detection, this approach provides scalable real-time monitoring while minimizing dependency on complex hardware. Additionally, this ML-driven framework allows for proactive network management, offering dynamic adaptability to network fluctuations. Our study emphasizes the promise of machine learning in optical network monitoring, paving the way for intelligent, self-optimizing systems that can support evolving communication demands and maintain superior service quality.