Modern data center networks provide multiple paths between any host pairs. Load balancing traffic across these paths is critical to achieve low latency and high throughput. Despite prior solutions show that flowlet-based solutions are powerful in achieving load balancing in asymmetric topology, they suffer from the congestion mismatch problem in rerouting flowlets across different paths. That is, due to lacking the explicit congestion feedback, existing flowlet-based solutions are difficult to utilize bandwidth resource efficiently, which may result in low link utilization and packet loss. In this paper, we propose a congestion-aware load balancing scheme named CAF to eliminate the problem of congestion mismatch. The basic idea behind CAF is that whenever the sender perceives flowlet switching, it proactively sends probe packets to measure available bandwidth and uses the measurement result to properly set the congestion window, avoiding the unnecessary under-utilization and packet loss. Through a series of large-scale NS2 simulations and testbed experiments, we demonstrate that CAF reduces average flow completion time by up to 86% compared with the state-of-the-art mechanisms.

现代数据中心网络在任何主机对之间提供了多条路径。这些路径之间的负载平衡流量对于实现低延迟和高吞吐量至关重要。尽管先前的解决方案显示了基于流的解决方案在非对称拓扑中实现负载平衡的功能强大，但是它们在跨不同路径重新路由流时仍存在拥塞不匹配的问题。也就是说，由于缺乏显式的拥塞反馈，现有的基于流程的解决方案难以有效地利用带宽资源，这可能导致链路利用率低和丢包。在本文中，我们提出了一种名为CAF的可感知拥塞的负载均衡方案，以消除拥塞不匹配的问题。CAF的基本思想是，每当发送方感知到流切换时，它主动发送探测数据包以测量可用带宽，并使用测量结果正确设置拥塞窗口，避免不必要的利用率不足和数据包丢失。通过一系列的大型NS2模拟和测试平台实验，我们证明了CAF与最新的机制相比，可将平均流程完成时间最多降低86％。