Internet companies are constructing large-scale AI clusters with commodity Ethernet switches for AI model training to support their businesses. AI training workloads impose stringent network requirements, mandating that cluster networks deliver high peak throughput while maintaining robustness and resilience in the face of link failures. We present SGLB, a distributed, global congestion-aware load balancing system for AI clusters. SGLB operates a control-plane protocol, SyncMesh, to enable a new load balancing abstraction in modern commodity switches—Global Load Balancing (GLB) engine—which utilizes global congestion information to distribute traffic across all available paths. We address three key challenges in designing SGLB: fast routing convergence to minimize downtime in the event of link failures, scalable maintenance of congestion profiles within the constraints of limited switch hardware resources, and preventing GLB throughput suppression in scenarios where path bandwidths are asymmetric. We prototype SGLB and conduct extensive experiments to evaluate SGLB. SGLB ensures rapid routing convergence in the event of link failures, recovering in as little as 45 μs to guarantee network robustness for long-term, stable model training. Additionally, SGLB effectively load-balances traffic across paths, avoiding those with global congestion, which accelerates All-to-All collective communication by up to 60%.

Magnetic Resonance (MR) imaging, despite its proven diagnostic utility, remains an inaccessible imaging modality for disease surveillance at the population level. A major factor rendering MR inaccessible is lengthy scan times. An MR scanner collects measurements associated with the underlying anatomy in the Fourier space, also known as the k-space. Creating a high-fidelity image requires collecting large quantities of such measurements, increasing the scan time. Traditionally to accelerate an MR scan, image reconstruction from under-sampled k-space data is the method of choice. However, recent works show the feasibility of bypassing image reconstruction and directly learning to detect disease directly from a sparser learned subset of the k-space measurements. In this work, we propose Adaptive Sampling for MR (ASMR), a sampling method that learns an adaptive policy to sequentially select k-space samples to optimize for target disease detection. On 6 out of 8 pathology classification tasks spanning the Knee, Brain, and Prostate MR scans, ASMR reaches within 2% of the performance of a fully sampled classifier while using only 8% of the k-space, as well as outperforming prior state-of-the-art work in k-space sampling such as EMRT, LOUPE, and DPS.

In this work, for the first time, we demonstrate that computers can automatically learn from observing the heuristic efforts of the last four decades, stand on the shoulders of the existing Internet congestion control (CC) schemes, and discover a better-performing one. To that end, we address many different practical challenges, from how to generalize representation of various existing CC schemes to serious challenges regarding learning from a vast pool of policies in the complex CC domain and introduce Sage. Sage is the first purely data-driven Internet CC design that learns a better scheme by harnessing the existing solutions. We compare Sage's performance with the state-of-the-art CC schemes through extensive evaluations on the Internet and in controlled environments. The results suggests that Sage has learned a better-performing policy. While there are still many unanswered questions, we hope our data-driven framework can pave the way for a more sustainable design strategy.

Can we instead of designing yet another new TCP algorithm, design a TCP plug-in that can enable machines to automatically boost the performance of the existing/future TCP designs in cellular networks? We answer this question by introducing DeepCC. DeepCC leverages advanced deep reinforcement learning (DRL) techniques to let machines automatically learn how to steer throughput-oriented TCP algorithms toward achieving applications' desired delays in a highly dynamic network such as the cellular network. We used DeepCC plug-in to boost the performance of various old and new TCP schemes including TCP Cubic, Google's BBR, TCP Westwood, and TCP Illinois in cellular networks. Through both extensive trace-based evaluations and real-world experiments, we show that not only DeepCC can significantly improve the performance of TCP schemes, but also after accompanied by DeepCC, these schemes can outperform state-of-the-art TCP protocols including new clean-slate machine learning-based designs and the ones designed solely for cellular networks.

These days, taking the revolutionary approach of using clean-slate learning-based designs to completely replace the classic congestion control schemes for the Internet is gaining popularity. However, we argue that current clean-slate learning-based techniques bring practical issues and concerns such as overhead, convergence issues, and low performance over unseen network conditions to the table. To address these issues, we take a pragmatic and evolutionary approach combining classic congestion control strategies and advanced modern deep reinforcement learning (DRL) techniques and introduce a novel hybrid congestion control for the Internet named Orca1. Through extensive experiments done over global testbeds on the Internet and various locally emulated network conditions, we demonstrate that Orca is adaptive and achieves consistent high performance in different network conditions, while it can significantly alleviate the issues and problems of its clean-slate learning-based counterparts.

Traditional Traffic Engineering (TE) solutions can achieve the optimal or near-optimal performance by rerouting as many flows as possible. However, they do not usually consider the negative impact, such as packet out of order, when frequently rerouting flows in the network. To mitigate the impact of network disturbance, one promising TE solution is forwarding the majority of traffic flows using Equal-Cost Multi-Path (ECMP) and selectively rerouting a few critical flows using Software-Defined Networking (SDN) to balance link utilization of the network. However, critical flow rerouting is not trivial because the solution space for critical flow selection is enormous. Moreover, it is impossible to design a heuristic algorithm for this problem based on fixed and simple rules, since rule-based heuristics are unable to adapt to the changes of the traffic matrix and network dynamics. In this paper, we propose CFR-RL (Critical Flow Rerouting-Reinforcement Learning), a Reinforcement Learning-based scheme that learns a policy to select critical flows for each given traffic matrix automatically. CFR-RL then reroutes these selected critical flows to balance link utilization of the network by formulating and solving a simple Linear Programming (LP) problem. Extensive evaluations show that CFR-RL achieves near-optimal performance by rerouting only 10%-21.3% of total traffic.