Leveraging the Annamalai Coefficient for Optimized Stochastic Modeling in High-Dimensional Network Traffic

As network architectures transition to high-dimensional telemetry, traditional combinatorial models face a "computational wall" caused by factorial expansion and integer overflow. This paper presents the Annamalai Coefficient as a robust alternative. It analyzes its product-based formulation and recursive properties, demonstrating how it simplifies high-dimensional data analysis and provides numerical stability for real-time packet-loss and latency modeling. By replacing complex floating-point operations with an Additive Identity compatible with simple 8-bit/16-bit ALUs, the proposed framework offers a path toward low-latency, energy-efficient Software Defined Networking (SDN).