3 research outputs found
Internet Congestion Control via Deep Reinforcement Learning
We present and investigate a novel and timely application domain for deep
reinforcement learning (RL): Internet congestion control. Congestion control is
the core networking task of modulating traffic sources' data-transmission rates
to efficiently utilize network capacity, and is the subject of extensive
attention in light of the advent of Internet services such as live video,
virtual reality, Internet-of-Things, and more. We show that casting congestion
control as RL enables training deep network policies that capture intricate
patterns in data traffic and network conditions, and leverage this to
outperform the state-of-the-art. We also highlight significant challenges
facing real-world adoption of RL-based congestion control, including fairness,
safety, and generalization, which are not trivial to address within
conventional RL formalism. To facilitate further research and reproducibility
of our results, we present a test suite for RL-guided congestion control based
on the OpenAI Gym interface.Comment: 10 pages, accepted to ICML 201
Towards A Learning-Based Framework for Self-Driving Design of Networking Protocols
Networking protocols are designed through long-time and hard-work human
efforts. Machine Learning (ML)-based solutions have been developed for
communication protocol design to avoid manual efforts to tune individual
protocol parameters. While other proposed ML-based methods mainly focus on
tuning individual protocol parameters (e.g., adjusting contention window), our
main contribution is to propose a novel Deep Reinforcement Learning (DRL)-based
framework to systematically design and evaluate networking protocols. We
decouple a protocol into a set of parametric modules, each representing a main
protocol functionality that is used as DRL input to better understand the
generated protocols design optimization and analyze them in a systematic
fashion. As a case study, we introduce and evaluate DeepMAC a framework in
which a MAC protocol is decoupled into a set of blocks across popular flavors
of 802.11 WLANs (e.g., 802.11 b/a/g/n/ac). We are interested to see what blocks
are selected by DeepMAC across different networking scenarios and whether
DeepMAC is able to adapt to network dynamics.Comment: 18 Pages, Under Review. arXiv admin note: text overlap with
arXiv:2002.02075, arXiv:2002.0379
Latency and Throughput Optimization in Modern Networks: A Comprehensive Survey
Modern applications are highly sensitive to communication delays and
throughput. This paper surveys major attempts on reducing latency and
increasing the throughput. These methods are surveyed on different networks and
surroundings such as wired networks, wireless networks, application layer
transport control, Remote Direct Memory Access, and machine learning based
transport control