The Internet has existed since the 1970s as a means of data exchange between network devices in small networks. In the early stage, there was a small number of devices, but today there is an ever-increasing number of devices, leading to congestion in the network. Therefore, congestion control has attracted so much attention in the academic community and the industry for the past 30 years. Recently, Google has developed BBR (Bottleneck Bandwidth and Round-Trip Time), a rate-based congestion control algorithm. BBR controls transmission rates based on delivery rate and round-trip time (RTT). However, such a static congestion control algorithm (e.g., BBR, etc.) cannot achieve high performance in various network conditions (e.g., low bandwidth, etc.). Concretely, these static algorithms cannot adapt to the dynamic changes of the network environment. Therefore, in this paper, we propose an adaptive algorithm (called ABBR) for congestion control in next-generation networks. ABBR takes into account the reinforcement learning algorithm to learn relevant policies to change the transmission rate corresponding to each congestion control algorithm to optimize long-term performance. The experimental results show that our proposal can achieve good performance in terms of throughput, RTT, and fairness compared to the benchmarks.

中文翻译：

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交通拥堵控制故障解决方案


互联网自 20 世纪 70 年代以来就已存在，作为小型网络中网络设备之间的数据交换手段。早期设备数量较少，现在设备数量不断增加，导致网络拥塞。因此，近30年来，拥塞控制引起了学术界和工业界的广泛关注。最近，Google开发了BBR（Bottleneck Bandwidth and Round-Trip Time），一种基于速率的拥塞控制算法。 BBR 根据传送速率和往返时间 (RTT) 控制传输速率。然而，这种静态拥塞控制算法（例如BBR等）无法在各种网络条件（例如低带宽等）下实现高性能。具体来说，这些静态算法无法适应网络环境的动态变化。因此，在本文中，我们提出了一种用于下一代网络拥塞控制的自适应算法（称为ABBR）。 ABBR考虑到强化学习算法来学习相关策略来改变每个拥塞控制算法对应的传输速率，以优化长期性能。实验结果表明，与基准相比，我们的提案在吞吐量、RTT 和公平性方面可以取得良好的性能。