Congestion control is a fundamental network task that modulates the data transmission rates of traffic sources to efficiently utilize network capacity. With the advent of machine learning, congestion control based on deep reinforcement learning is the subject of extensive attention. At present, research on machine-learning-based congestion control is mainly focused on single-task scenarios -- to design a control strategy that is only used to reduce congestion. This article studies the congestion control problem based on machine learning in multi-task scenarios. Specifically, we propose a congestion control model based on multi-task deep reinforcement learning. The model takes congestion control as the main task and load balancing as the auxiliary task. Compared to the single-task method, our model can better represent the network environment by learning the shared representation of congestion features and load balancing features. Moreover, network traffic control may involve both congestion control and load balancing. Therefore, learning multiple tasks jointly while exploiting commonalities and differences across tasks can help reduce the cost of task coordination. We use software defined networking to decouple the data and control planes, making network control strategies more flexible than traditional networks. To the best of our knowledge, this is the first time multi-task learning has been applied to network traffic control. Experimental results show that the method is efficient.

中文翻译：

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通过多任务深度强化学习实现基于 SDN 的网络拥塞控制


拥塞控制是一项基本的网络任务，它调节流量源的数据传输速率以有效地利用网络容量。随着机器学习的出现，基于深度强化学习的拥塞控制受到广泛关注。目前，基于机器学习的拥塞控制研究主要集中在单任务场景——设计一种仅用于减少拥塞的控制策略。本文研究多任务场景下基于机器学习的拥塞控制问题。具体来说，我们提出了一种基于多任务深度强化学习的拥塞控制模型。该模型以拥塞控制为主要任务，负载均衡为辅助任务。与单任务方法相比，我们的模型通过学习拥塞特征和负载平衡特征的共享表示可以更好地表示网络环境。此外，网络流量控制可能涉及拥塞控制和负载平衡。因此，联合学习多个任务，同时利用任务之间的共性和差异，有助于降低任务协调的成本。我们使用软件定义网络来解耦数据平面和控制平面，使网络控制策略比传统网络更加灵活。据我们所知，这是多任务学习首次应用于网络流量控制。实验结果表明该方法是有效的。