Dapper: Deploying Service Function Chains in the Programmable Data Plane Via Deep Reinforcement Learning

Network functions perform specific packet processing on network traffic. To meet operators’ needs, forming service function chains (SFCs) is a fundamental technique used in today's ISPs and datacenter networks. Implementing SFCs in the programmable data plane with high throughput and low latency is a new approach to satisfy demands of ever-growing network traffic. Previous works have proposed different solutions to solve the problem, but they all inevitably have to make trade-offs between running time and performance. For example, an ILP (Integer Linear Programming) can optimize cost but suffers from long running time in large-scale network topologies. Heuristic algorithms depend strongly on manual designs and usually have a performance gap with the optimal solution. In this paper, we propose
Dapper
, a framework for deploying SFCs in the programmable data plane using DRL (Deep Reinforcement Learning) with graph convolutional network. In order to expand the searching space to prevent the optimal value from being missed,
Dapper
allows the RL (Reinforcement Learning) agent to simultaneously extract features from both the substrate network and the hardware pipeline, and exploit a graph convolutional network to enhance performance. Moreover, a mask mechanism is also designed to accelerate
Dapper
and improve its scalability.
Dapper
has been implemented and extensively evaluated on both P4 hardware switches (equipped with Intel Tofino ASIC) and software switches (i.e., bmv2). Experimental results show that
Dapper
can automatically generate deployment solutions in a few seconds of running time after training. They also demonstrate that
Dapper
reduces hardware stage usage and the latency of SFCs by up to 17.8% and 50
$\sim$
73% respectively on average when compared with heuristics.