Applying Reinforcement Learning to Packet Scheduling in Routers
Herman Ferrá, K.M. Lau, Christopher A. Leckie, Anderson Tang · 2003
An important problem for the Internet is how to provide a guaranteed quality of service to users, in contrast to the cur-rent “best-effort ” service. A key aspect of this problem is how routers should share network capacity between different classes of traffic. This decision needs to be made for each in-coming packet, and is known as the packet scheduling prob-lem. A major challenge in packet scheduling is that the be-haviour of each traffic class may not be known in advance, and can vary dynamically. In this paper, we describe how we have modelled the packet scheduling problem as an appli-cation for reinforcement learning (RL). We demonstrate how our RL approach can learn scheduling policies that satisfy the quality of service requirements of multiple traffic classes un-der a variety of conditions. We also present an insight into the effectiveness of two different RL algorithms in this context. A major benefit of this approach is that we can help network providers deliver a guaranteed quality of service to customers without manual fine-tuning of the network routers.