Research on Content-Aware Data Routing and Congestion Control Algorithms for Soft Buses

Faced with expanding scale and heterogeneity in distributed systems, conventional soft buses encounter significant scheduling bottlenecks during high-concurrency, multi-content operations. To address semantic data variability and boost throughput, this study develops a content-aware routing and congestion control algorithm. Our approach incorporates: multidimensional content tagging and ontology modeling enabling dynamic hotspot identification via semantic clustering; a path computation mechanism balancing content affinity and node load for optimized routing; online learning-driven routing table optimization with experience transfer for enhanced node adaptability; distributed congestion detection triggering content-differentiated rate control; and Q-learning policy optimization for self-adaptive rate regulation. NS-3 platform validation demonstrates superior performance—yielding 29% higher content hit rates, 31% improved forwarding efficiency, and 32% faster congestion response versus baselines—while enhancing soft bus stability and intelligence in dynamic environments as observed in China Southern Grid case studies.