Abstract
Data center networks demand high-performance, robust, and practical data plane load balancing protocols. Despite progress, existing work falls short of satisfying these requirements. We design and evaluate Luopan, a novel sampling based load balancing protocol that overcomes these challenges. Luopan operates at flowcell granularity similar to Presto. It periodically samples a few paths to each destination switch and directs flowcells to the least congested one. By being congestion-aware, Luopan improves flow completion time (FCT), and is more robust to topological asymmetries compared to Presto. The sampling approach simplifies the protocol and makes it much more scalable for implementation in large-scale networks compared to existing congestion-aware schemes. We conduct comprehensive packet-level simulations with a production workload. The results show that Luopan consistently outperforms state-of-the-art schemes in large-scale symmetric and asymmetric topologies. Compared to Presto, Luopan with 2 samples improves the 99%ile FCT of mice flows by up to 45%, and average FCT of medium flows by ∼20%.