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Fair packet enqueueing and marking in multi-queue datacenter networks
Recently, Explicit Congestion Notification (ECN) has been leveraged by most Datacenter Network (DCN) protocols for congestion control to achieve high throughput and low latency. However, the majority of these approaches assume that each switch port has one queue while current industry trends towards having multiple queues per switch port. To this end, we propose ML-ECN, a fairness-aware packet enqueueing and multi-level probabilistic ECN marking scheme for DCNs enabled with multiple-service, multiple-queue switch ports. The main design of ML-ECN relies on the separation between small, medium, and large flows by dedicating multiple queues for each flow class to ensure fair enqueueing. ML-ECN employs one ECN marking threshold for the small queue class and multiple thresholds with a probabilistic marking for the medium and large queue classes to achieve low latency for mice (small) and high throughput for elephant (large) flows. In addition, ML-ECN performs fairness-aware ECN marking that ensures that packets of short flows are not getting marked due to buffer buildups caused by longer flows. Large-scale ns-2 simulations show that ML-ECN outperforms existing approaches at different performance metrics