Semantic-Driven Traffic Shaping Contract for AI Networks

This document defines a "Semantic-Driven Shaping Contract". Traditional network protocols treat AI training and inference traffic as opaque byte streams, leading to highly inefficient scheduling. This contract allows applications or distributed training frameworks to explicitly pass "minimum necessary semantics" to the underlying network. In exchange, the network commits to executing fine-grained, differentiated forwarding and resource allocation actions for tensor flows with diverse semantics, based on predefined rules and global real-time states. This model significantly improves overall resource utilization and task completion times in heterogeneous computing networks, cross-domain intelligent computing centers, and integrated training-inference scenarios.