🤖 AI Summary
To address congestion and high tail latency caused by insufficient coordination between multipath routing and queue scheduling in Fat-Tree data center networks, this paper proposes a lightweight joint feedback framework that enables, for the first time, real-time co-adaptation of routing decisions and queue state. The method integrates a distributed adaptive routing algorithm—leveraging local link load and per-queue length—with ECN-enhanced Active Queue Management (AQM). This coupling supports buffer-aware dynamic path selection and fine-grained traffic steering. NS-3 evaluations demonstrate that, compared to ECMP+PIFO, the approach improves average throughput by 23%, reduces the 99.9th-percentile flow completion time by 41%, and incurs control overhead below 1.5%. The framework thus significantly enhances the trade-off between throughput and tail latency.