Over the years, the Internet has been enriched with new available communication
technologies, for both fixed and mobile networks and devices, exhibiting an impressive …

Emerging wireless systems target to provide multi-Gbps data rates for each user, which can
be achieved by utilizing ultra-wide channels available at mmWave, terahertz, and lightwave …

Internet transport algorithms are foundational to the performance of network applications.
But a number of practical challenges make it difficult to evaluate new ideas and algorithms in …

We present Park, a platform for researchers to experiment with Reinforcement Learning (RL)
for computer systems. Using RL for improving the performance of systems has a lot of …

This short paper presents a detailed empirical study of BBR's performance under different
real-world and emulated testbeds across a range of network operating conditions. Our …

The web serving protocol stack is constantly evolving to tackle the technological shifts in
networking infrastructure and website complexity. As a result of this evolution, web servers …

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 …

Multipath transport, as embodied in MPTCP, is deployed to improve throughput and
reliability in mobile and residential access networks, with additional use-cases including …

Networking protocols are designed through long-standing and hard-working human efforts.
Machine Learning (ML)-based solutions for communication protocol design have been …

Recent networking research has identified that data-driven congestion control (CC) can be
more efficient than traditional CC in TCP. Deep reinforcement learning (RL), in particular …