@INPROCEEDINGS{Angi2405:ROAR,
AUTHOR="Antonino Angi and Alessio Sacco and Flavio Esposito and Guido Marchetto",
TITLE="{ROAR:} Routing Packets in {P4} Switches With {Multi-Agent} Decisions Logic",
BOOKTITLE="2024 IEEE International Conference on Machine Learning for Communication
and Networking (ICMLCN) (2024 ICMLCN)",
ADDRESS="Stockholm, Sweden",
PAGES="5.98",
DAYS=4,
MONTH=may,
YEAR=2024,
ABSTRACT="The soaring complexity of networks has led to more complex methods to
efficiently manage and orchestrate the multitude of network environments.
Recent advances in machine learning (ML) have opened new opportunities for
network management automation, exploiting existing advances in
software-defined infrastructures. Advanced routing strategies have been
proposed to accommodate the traffic demand of interactive systems, where
the common architecture is composed of a data-driven network management
schema collecting network data that feed a reinforcement learning (RL)
algorithm. However, the overhead introduced by the SDN controller and its
operations can be mitigated if the networking architecture is redesigned.
In this paper, we propose ROAR, a novel architectural solution that
implements Deep Reinforcement Learning (DRL) inside P4 programmable
switches to perform adaptive routing policies based on network conditions
and traffic patterns. The network devices act independently in a
multi-agent reinforcement learning (MARL) framework but are able to learn
cooperative behaviors to reduce the queuing time of transmitting packets.
Experimental results show that for an increasing amount of traffic in the
network, there is both a throughput and delay improvement in the
transmission compared to traditional approaches."
}