Modern data center networks typically adopt multi-rooted tree topologies such leaf-spine and fat-tree to provide high bisection bandwidth. Load balancing is critical to achieve low latency and high throughput. Although the per-packet schemes such as Random Packet Spraying (RPS) can achieve high network utilization and near-optimal tail latency in symmetric topologies, they are prone to cause significant packet reordering and degrade the network performance. Moreover, some coding-based schemes are proposed to alleviate the problem of packet reordering and loss. Unfortunately, these schemes ignore the traffic characteristics of data center network and cannot achieve good network performance. In this paper, we propose a Heterogeneous Traffic-aware Partition Coding named HTPC to eliminate the impact of packet reordering and improve the performance of short and long flows. HTPC smoothly adjusts the number of redundant packets based on the multi-path congestion information and the traffic characteristics so that the tailing probability of short flows and the timeout probability of long flows can be reduced. Through a series of large-scale NS2 simulations, we demonstrate that HTPC reduces average flow completion time by up to 60% compared with the state-of-the-art mechanisms.

中文翻译：

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HTPC：用于数据中心网络中随机数据包喷射的异构流量感知分区编码

现代数据中心网络通常采用多根树拓扑，如叶脊和胖树，以提供高二分带宽。负载平衡对于实现低延迟和高吞吐量至关重要。尽管随机包喷射（RPS）等逐包方案可以在对称拓扑中实现高网络利用率和接近最优的尾部延迟，但它们容易导致显着的包重新排序并降低网络性能。此外，还提出了一些基于编码的方案来缓解数据包重新排序和丢失的问题。遗憾的是，这些方案忽略了数据中心网络的流量特性，无法实现良好的网络性能。在本文中，我们提出了一种名为 HTPC 的异构流量感知分区编码，以消除数据包重新排序的影响并提高短流和长流的性能。HTPC根据多径拥塞信息和流量特性平滑调整冗余包的数量，从而降低短流的拖尾概率和长流的超时概率。通过一系列大规模的 NS2 模拟，我们证明与最先进的机制相比，HTPC 将平均流动完成时间减少了 60%。