ALB: Adaptive Load Balancing Based on Accurate Congestion Feedback for Asymmetric Topologies

In datacenter networks, multipath exists to facilitate parallel data transmission. Taking deployment challenges into account, some optimized alternatives (e.g. CLOVE, Hermes) to ECMP balance load at the virtual edge or hosts. However inaccuracies of congestion detection and reaction exist in these solutions. They either detect congestion through ECN and coarse-grained RTT measurements or are congestion-oblivious. These congestion feedbacks are not sufficient enough to indicate the accurate congestion status under asymmetry. And when rerouting events occur on multiple paths, ACKs with congestion feedback of other paths can improperly influence the current sending rate. Therefore, we explore how to balance load by solving above inaccuracy problems while ensuring good adaptation to commodity switches and existing network protocols. We propose ALB, an adaptive load-balancing mechanism based on accurate congestion feedback running at end hosts, which is resilient to asymmetry. ALB leverage a latency-based congestion detection to precisely route flowlets to lighter load paths, and an ACK correction method to avoid inaccurate flow rate adjustment. In large-scale simulations ALB achieves up to 7% and 40% better flow completion time (FCT) than CONGA and CLOVE-ECN under asymmetry.