Mobile wireless networks present several challenges for any learning system, due to uncertain and variable device movement, a decentralized network architecture, and constraints on network resources. In this work, we use deep reinforcement learning (DRL) to learn a scalable and generalizable forwarding strategy for such networks. We make the following contributions: i) we use hierarchical RL to design DRL packet agents rather than device agents, to capture the packet forwarding decisions that are made over time and improve training efficiency; ii) we use relational features to ensure generalizability of the learned forwarding strategy to a wide range of network dynamics and enable offline training; and iii) we incorporate both forwarding goals and network resource considerations into packet decision-making by designing a weighted DRL reward function. Our results show that our DRL agent often achieves a similar delay per packet delivered as the optimal forwarding strategy and outperforms all other strategies including state-of-the-art strategies, even on scenarios on which the DRL agent was not trained.
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Lessons from the evolution of the Batfish configuration analysis tool
Batfish is a tool to analyze network configurations and forwarding. It has evolved from a research prototype to an industrial-strength product, guided by scalability, fidelity, and usability challenges encountered when analyzing complex, real-world networks. We share key lessons from this evolution, including how Datalog had significant limitations when generating and analyzing forwarding state and how binary decision diagrams (BDDs) proved highly versatile. We also describe our new techniques for addressing real- world challenges, which increase Batfish performance by three orders of magnitude and enable high-fidelity analysis of networks with thousands of nodes within minutes.
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- Award ID(s):
- 2219862
- NSF-PAR ID:
- 10534020
- Publisher / Repository:
- ACM
- Date Published:
- ISBN:
- 9798400702365
- Page Range / eLocation ID:
- 122 to 135
- Subject(s) / Keyword(s):
- Network verification configuration analysis Batfish
- Format(s):
- Medium: X
- Location:
- New York NY USA
- Sponsoring Org:
- National Science Foundation
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- Free Publicly Accessible Full Text
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Accepted Manuscript1.0
- Conference Paper:
- https://doi.org/10.1145/3603269.3604866
