ENERO: Efficient Real-Time Routing Optimization
- URL: http://arxiv.org/abs/2109.10883v1
- Date: Wed, 22 Sep 2021 17:53:30 GMT
- Title: ENERO: Efficient Real-Time Routing Optimization
- Authors: Paul Almasan, Shihan Xiao, Xiangle Cheng, Xiang Shi, Pere Barlet-Ros,
Albert Cabellos-Aparicio
- Abstract summary: Traffic Engineering (TE) solutions must be able to achieve high performance real-time network operation.
Current TE technologies rely on hand-crafteds or computationally expensive solvers.
We propose Enero, an efficient real-time TE engine.
- Score: 2.830334160074889
- License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
- Abstract: Wide Area Networks (WAN) are a key infrastructure in today's society. During
the last years, WANs have seen a considerable increase in network's traffic as
well as in the number of network applications. To enable the deployment of
emergent network applications (e.g., Vehicular networks, Internet of Things),
existing Traffic Engineering (TE) solutions must be able to achieve high
performance real-time network operation. In addition, TE solutions must be able
to adapt to dynamic scenarios (e.g., changes in the traffic matrix or topology
link failures). However, current TE technologies rely on hand-crafted
heuristics or computationally expensive solvers, which are not suitable for
highly dynamic TE scenarios.
In this paper we propose Enero, an efficient real-time TE engine. Enero is
based on a two-stage optimization process. In the first one, it leverages Deep
Reinforcement Learning (DRL) to optimize the routing configuration by
generating a long-term TE strategy. We integrated a Graph Neural Network (GNN)
into the DRL agent to enable efficient TE on dynamic networks. In the second
stage, Enero uses a Local Search algorithm to improve DRL's solution without
adding computational overhead to the optimization process. Enero offers a lower
bound in performance, enabling the network operator to know the worst-case
performance of the DRL agent. We believe that the lower bound in performance
will lighten the path of deploying DRL-based solutions in real-world network
scenarios. The experimental results indicate that Enero is able to operate in
real-world dynamic network topologies in 4.5 seconds on average for topologies
up to 100 edges.
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