The increasingly complicated and diverse applications have distinct network performance demands, e.g., some desire high throughput while others require low latency. Traditional congestion controls (CC) have no perception of these demands. Consequently, literatures have explored the objective-specific algorithms, which are based on either offline training or online learning, to adapt to certain application demands. However, once generated, such algorithms are tailored to a specific performance objective function. Newly emerged performance demands in a changeable network environment require either expensive retraining (in the case of offline training), or manually redesigning a new objective function (in the case of online learning). To address this problem, we propose a novel architecture, DeepCC. It generates a CC agent that is generically applicable to a wide range of application requirements and network conditions. The key idea of DeepCC is to leverage both offline deep reinforcement learning and online fine-tuning. In the offline phase, instead of training towards a specific objective function, DeepCC trains its deep neural network model using multi-objective optimization. With the trained model, DeepCC offers near Pareto optimal policies w.r.t different user-specified trade-offs between throughput, delay, and loss rate without any redesigning or retraining. In addition, a quick online fine-tuning phase further helps DeepCC achieve the application-specific demands under dynamic network conditions. The simulation and real-world experiments show that DeepCC outperforms state-of-the-art schemes in a wide range of settings. DeepCC gains a higher target completion ratio of application requirements up to 67.4% than that of other schemes, even in an untrained environment.

中文翻译：

---

DeepCC：通过多目标优化弥合拥塞控制和应用之间的差距

日益复杂和多样化的应用程序具有不同的网络性能需求，例如，一些需要高吞吐量而另一些则需要低延迟。传统的拥塞控制 (CC) 没有意识到这些需求。因此，文献探索了基于离线训练或在线学习的目标特定算法，以适应某些应用需求。但是，一旦生成，此类算法将针对特定的性能目标函数进行定制。在多变的网络环境中新出现的性能需求需要昂贵的再训练（在离线训练的情况下），或者手动重新设计新的目标函数（在在线学习的情况下）。为了解决这个问题，我们提出了一种新颖的架构 DeepCC。它生成一个 CC 代理，它通常适用于广泛的应用要求和网络条件。DeepCC 的关键思想是利用离线深度强化学习和在线微调。在离线阶段，DeepCC 不是针对特定目标函数进行训练，而是使用多目标优化来训练其深度神经网络模型。使用经过训练的模型，DeepCC 提供接近帕累托最优策略，在吞吐量、延迟和丢失率之间进行不同的用户指定权衡，而无需任何重新设计或重新训练。此外，快速的在线微调阶段进一步帮助 DeepCC 实现动态网络条件下的特定应用需求。模拟和现实世界的实验表明，DeepCC 在广泛的设置中优于最先进的方案。