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Luopan: Sampling based load balancing in data center networks

机译：Luopan：数据中心网络中的基于加载负载平衡

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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%.
机译：数据中心网络需要高性能，强大，实用的数据平面负载平衡协议。尽管进步，现有工作缺乏满足这些要求。我们设计和评估Luopan，这是一种基于新型的加载平衡协议，克服了这些挑战。罗潘在流动的粒度下运行，类似于Presto。它定期对每个目的地交换机进行一些路径，并将流单元指向最小拥挤的路径。通过充血感知，Luopan改善了流程完成时间（FCT），与Presto相比，对拓扑非对称的更鲁棒。采样方法简化了协议，与现有拥塞式方案相比，在大型网络中实现更加可扩展。我们通过生产工作量进行全面的数据包级模拟。结果表明，随着大规模对称和不对称拓扑的最先进的方案，洛邦始终如一地优于最先进的方案。与presto相比，Luopan用2个样品改善了小鼠的99％的小鼠FCT，高达45％，培养基流量平均FCT〜20％。

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