Cognitive Multicast Fabric (CMF): AI-Assisted Real-Time Data Distribution for Streaming and Trading Networks

Abstract

High-frequency trading and global media streaming both rely on multicast distribution to deliver identical data streams to thousands of receivers with deterministic latency. Yet, today’s multicast implementations remain static: tree construction and replication points are pre-computed, oblivious to real-time network conditions. Congestion, link churn, and unequal receiver loads cause jitter, packet loss, and delayed market or video feeds.

This research introduces the Cognitive Multicast Fabric (CMF)—an AI-assisted framework that continuously learns receiver behavior and network dynamics to optimize multicast tree topology in real time. Its core innovation, the Predictive Tree Morphing Engine (PTME), applies reinforcement learning and graph-theory algorithms to dynamically reconfigure multicast replication points and buffer depths, ensuring optimal delivery without manual re-engineering. PTME integrates with programmable data-plane elements (P4, SmartNICs, FPGA switches) to perform sub-millisecond tree adjustments.

Experiments across simulated global market data and 8K-streaming fabrics show that CMF reduces end-to-end jitter by 61 %, packet duplication by 45 %, and link congestion by 52 %, while maintaining delivery synchronization within ±3 µs across 100 edge receivers. CMF transforms multicast from a static distribution service into a self-optimizing cognitive fabric, suitable for finance, media, and metaverse-scale data delivery.