arXiv subjects

Francis Y. Yan

Publications and source records attributed to Francis Y. Yan.

17 recordsLinked to original sources

arXiv2026

A DualPI2 Module for Mahimahi: Behavioral Characterization and Cross-Platform Analysis

Low Latency, Low Loss, and Scalable Throughput (L4S) is an emerging paradigm for latency control based on DualPI2 active queue management and scalable congestion control. While a Linux kernel implementation of DualPI2 is available, controlled and reproducible experimentation on L4S mechanisms can be facilitated by a modular, user-space alternative. In this paper, we present a DualPI2 module for the Mahimahi network emulator, designed to support extensible, component-level experimentation without kernel modification. We conduct a statistical behavioral characterization of the Mahimahi implementation by examining key metrics across diverse traffic patterns and network conditions, using the Linux kernel implementation as a reference baseline. Our analysis shows that behavioral alignment across execution environments is not automatic: identical DualPI2 parameterization does not guarantee identical dynamics. Instead, key control parameters exhibit environment-dependent sensitivity, leading to regime-dependent discrepancies across bandwidth-delay product (BDP) conditions. Through targeted parameter exploration, we identify configurations that improve cross-platform alignment in low BDP regimes, while revealing structural differences that persist under higher load. This work provides both a practical tool for experimental L4S research and empirical insight into cross-platform behavioral differences, highlighting the importance of systematic characterization and environment-aware parameter selection in emulation-based AQM studies.

cs.NI↗

arXiv2026

AutoSpec: Automated Generation of Neural Network Specifications

The increasing adoption of neural networks in learning-augmented systems highlights the growing need for model safety and robustness, especially in safety-critical domains. While recent advances in neural network verification offer formal guarantees on worst-case behavior, existing approaches require users to manually define model specifications, an error-prone, incomplete, and time-consuming process. In this paper, we present AutoSpec, the first comprehensive framework for automatically generating and evaluating neural network specifications for learning-augmented systems. AutoSpec introduces a tree-based algorithm that adaptively partitions the input space to generate specification sets aligned with model behavior, as well as a statistical certification framework that provides rigorous accuracy guarantees for each specification. We also propose a principled evaluation framework that defines interpretable metrics for specification accuracy and coverage, establishing a benchmark for future research. Experiments across four diverse applications show that AutoSpec outperforms both manually defined specifications and existing baseline algorithms, improving the F1 score by up to 53% over human-defined specifications and 73% over the strongest baseline.

cs.LG↗

arXiv2026

MetaMuse: Algorithm Generation via Creative Ideation

Designing system algorithms remains challenging, where the discontinuous nature of the solution space often forces system engineers to rely on generic heuristics at the expense of performance. We study whether LLMs can practically drive algorithm generation, and find that they are biased towards well-known generic designs, rather than making the creative leaps needed to navigate the discontinuous solution space. To address this limitation, we introduce MetaMuse, a framework for creative ideation built on three self-reflection principles: (1) quantifying solution diversity and usefulness in measurable performance space, rather than abstract idea space, (2) steering ideation through external stimuli, rather than internal randomness, and (3) constructing executable solutions using waypoint reasoning, rather than free-form chain-of-thought. Considering two critical online problems at a global cloud provider, extensive evaluations show that MetaMuse can generate high-performing solutions: it reduces cache misses by up to 35.76% in cache replacement and reduces bin usage by up to 30.93% in online bin packing.

cs.AI↗

arXiv2026

Offline Meta-learning for Real-time Bandwidth Estimation

Real-time video applications require dynamic bitrate adjustments based on network capacity, necessitating accurate bandwidth estimation (BWE). We introduce Ivy, a novel BWE method that leverages offline meta-learning to combat data drift and maximize user Quality of Experience (QoE). Our approach dynamically selects the most suitable BWE algorithm for current network conditions, enabling effective adaptation to changing environments without requiring live network interactions. We implemented our method in Microsoft Teams and demonstrated that Ivy can enhance QoE by 5.9% to 11.2% over individual BWE algorithms and by 6.3% to 11.4% compared to existing online meta heuristics. Additionally, we show that our method is more data efficient compared to online meta-learning methods, achieving up to 21% improvement in QoE while requiring significantly less training data.

cs.NI↗

arXiv2026

NetArena: Dynamic Benchmarks for AI Agents in Network Automation

As AI agents expand into high-stakes domains like network system operations, evaluating their real-world reliability becomes increasingly critical. However, existing benchmarks risk contamination due to static design, show high statistical variance from limited dataset size, and fail to reflect the complexity of production environments. We present NetArena, a dynamic benchmark generation framework for network applications. NetArena introduces a novel abstraction and unified interface that generalize across diverse tasks, enabling dynamic benchmarking despite the heterogeneity of network workloads. At runtime, users can generate unlimited queries on demand. NetArena integrates with network emulators to measure correctness, safety, and latency during execution. We demonstrate NetArena on three representative applications and find that (1) NetArena significantly improves statistical reliability across AI agents, reducing confidence-interval overlap from 85% to 0, (2) agents achieve only 13-38% average performance (as low as 3%) for large-scale, realistic queries, and (3) it exposes more fine-grained behaviors that static, correctness-only benchmarks miss. NetArena also enables use cases such as SFT and RL fine-tuning on network system tasks. Code is available at https://github.com/Froot-NetSys/NetArena.

cs.NI↗

arXiv2025

Congestion Control System Optimization with Large Language Models

Congestion control is a fundamental component of Internet infrastructure, and researchers have dedicated considerable effort to developing improved congestion control algorithms. However, despite extensive study, existing algorithms continue to exhibit suboptimal performance across diverse network environments. In this paper, we introduce a novel approach that automatically optimizes congestion control algorithms using large language models (LLMs). Our framework consists of a structured algorithm generation process, an emulation-based evaluation pipeline covering a broad range of network conditions, and a statistically guided method to substantially reduce evaluation time. Empirical results from four distinct LLMs validate the effectiveness of our approach. We successfully identify algorithms that achieve up to 27% performance improvements over the original BBR algorithm in a production QUIC implementation. Our work demonstrates the potential of LLMs to accelerate the design of high-performance network algorithms and paves the way for broader applications in networking systems.

cs.NI↗

arXiv2025

Decouple and Decompose: Scaling Resource Allocation with DeDe

Efficient resource allocation is essential in cloud systems to facilitate resource sharing among tenants. However, the growing scale of these optimization problems have outpaced commercial solvers commonly employed in production. To accelerate resource allocation, prior approaches either customize solutions for narrow domains or impose workload-specific assumptions. In this work, we revisit real-world resource allocation problems and uncover a common underlying structure: the vast majority of these problems are inherently separable, i.e., they optimize the aggregate utility of individual resource and demand allocations, under separate constraints for each resource and each demand. Building on this observation, we develop DeDe, a scalable and theoretically rooted optimization framework for large-scale resource allocation. At the core of DeDe is a decouple-and-decompose approach: it decouples entangled resource and demand constraints and thereby decomposes the overall optimization into alternating per-resource and per-demand subproblems that can be solved efficiently and in parallel. We have implemented and released DeDe as a Python package with a familiar modeling interface. Our experiments on three representative resource allocation tasks -- cluster scheduling, traffic engineering, and load balancing -- demonstrate that DeDe delivers significant speedups while generating higher-quality allocations.

cs.DC↗

arXiv2025

Mowgli: Passively Learned Rate Control for Real-Time Video

Rate control algorithms are at the heart of video conferencing platforms, determining target bitrates that match dynamic network characteristics for high quality. Recent data-driven strategies have shown promise for this challenging task, but the performance degradation they introduce during training has been a nonstarter for many production services, precluding adoption. This paper aims to bolster the practicality of data-driven rate control by presenting an alternative avenue for experiential learning: leveraging purely existing telemetry logs produced by the incumbent algorithm in production. We observe that these logs contain effective decisions, although often at the wrong times or in the wrong order. To realize this approach despite the inherent uncertainty that log-based learning brings (i.e., lack of feedback for new decisions), our system, Mowgli, combines a variety of robust learning techniques (i.e., conservatively reasoning about alternate behavior to minimize risk and using a richer model formulation to account for environmental noise). Across diverse networks (emulated and real-world), Mowgli outperforms the widely deployed GCC algorithm, increasing average video bitrates by 15-39% while reducing freeze rates by 60-100%.

cs.NI↗

arXiv2025

AquaScope: Reliable Underwater Image Transmission on Mobile Devices

Underwater communication is essential for both recreational and scientific activities, such as scuba diving. However, existing methods remain highly constrained by environmental challenges and often require specialized hardware, driving research into more accessible underwater communication solutions. While recent acoustic-based communication systems support text messaging on mobile devices, their low data rates severely limit broader applications. We present AquaScope, the first acoustic communication system capable of underwater image transmission on commodity mobile devices. To address the key challenges of underwater environments -- limited bandwidth and high transmission errors -- AquaScope employs and enhances generative image compression to improve compression efficiency, and integrates it with reliability-enhancement techniques at the physical layer to strengthen error resilience. We implemented AquaScope on the Android platform and demonstrated its feasibility for underwater image transmission. Experimental results show that AquaScope enables reliable, low-latency image transmission while preserving perceptual image quality, across various bandwidth-constrained and error-prone underwater conditions.

cs.NI↗

arXiv2024

Designing Network Algorithms via Large Language Models

We introduce NADA, the first framework to autonomously design network algorithms by leveraging the generative capabilities of large language models (LLMs). Starting with an existing algorithm implementation, NADA enables LLMs to create a wide variety of alternative designs in the form of code blocks. It then efficiently identifies the top-performing designs through a series of filtering techniques, minimizing the need for full-scale evaluations and significantly reducing computational costs. Using adaptive bitrate (ABR) streaming as a case study, we demonstrate that NADA produces novel ABR algorithms -- previously unknown to human developers -- that consistently outperform the original algorithm in diverse network environments, including broadband, satellite, 4G, and 5G.

cs.NI↗

arXiv2024

Teal: Learning-Accelerated Optimization of WAN Traffic Engineering

The rapid expansion of global cloud wide-area networks (WANs) has posed a challenge for commercial optimization engines to efficiently solve network traffic engineering (TE) problems at scale. Existing acceleration strategies decompose TE optimization into concurrent subproblems but realize limited parallelism due to an inherent tradeoff between run time and allocation performance. We present Teal, a learning-based TE algorithm that leverages the parallel processing power of GPUs to accelerate TE control. First, Teal designs a flow-centric graph neural network (GNN) to capture WAN connectivity and network flows, learning flow features as inputs to downstream allocation. Second, to reduce the problem scale and make learning tractable, Teal employs a multi-agent reinforcement learning (RL) algorithm to independently allocate each traffic demand while optimizing a central TE objective. Finally, Teal fine-tunes allocations with ADMM (Alternating Direction Method of Multipliers), a highly parallelizable optimization algorithm for reducing constraint violations such as overutilized links. We evaluate Teal using traffic matrices from Microsoft's WAN. On a large WAN topology with >1,700 nodes, Teal generates near-optimal flow allocations while running several orders of magnitude faster than the production optimization engine. Compared with other TE acceleration schemes, Teal satisfies 6--32% more traffic demand and yields 197--625x speedups.

cs.NI↗

arXiv2024

Autothrottle: A Practical Bi-Level Approach to Resource Management for SLO-Targeted Microservices

Achieving resource efficiency while preserving end-user experience is non-trivial for cloud application operators. As cloud applications progressively adopt microservices, resource managers are faced with two distinct levels of system behavior: end-to-end application latency and per-service resource usage. Translating between the two levels, however, is challenging because user requests traverse heterogeneous services that collectively (but unevenly) contribute to the end-to-end latency. We present Autothrottle, a bi-level resource management framework for microservices with latency SLOs (service-level objectives). It architecturally decouples application SLO feedback from service resource control, and bridges them through the notion of performance targets. Specifically, an application-wide learning-based controller is employed to periodically set performance targets -- expressed as CPU throttle ratios -- for per-service heuristic controllers to attain. We evaluate Autothrottle on three microservice applications, with workload traces from production scenarios. Results show superior CPU savings, up to 26.21% over the best-performing baseline and up to 93.84% over all baselines.

cs.DC↗

arXiv2024

ACM MMSys 2024 Bandwidth Estimation in Real Time Communications Challenge

The quality of experience (QoE) delivered by video conferencing systems to end users depends in part on correctly estimating the capacity of the bottleneck link between the sender and the receiver over time. Bandwidth estimation for real-time communications (RTC) remains a significant challenge, primarily due to the continuously evolving heterogeneous network architectures and technologies. From the first bandwidth estimation challenge which was hosted at ACM MMSys 2021, we learned that bandwidth estimation models trained with reinforcement learning (RL) in simulations to maximize network-based reward functions may not be optimal in reality due to the sim-to-real gap and the difficulty of aligning network-based rewards with user-perceived QoE. This grand challenge aims to advance bandwidth estimation model design by aligning reward maximization with user-perceived QoE optimization using offline RL and a real-world dataset with objective rewards which have high correlations with subjective audio/video quality in Microsoft Teams. All models submitted to the grand challenge underwent initial evaluation on our emulation platform. For a comprehensive evaluation under diverse network conditions with temporal fluctuations, top models were further evaluated on our geographically distributed testbed by using each model to conduct 600 calls within a 12-day period. The winning model is shown to deliver comparable performance to the top behavior policy in the released dataset. By leveraging real-world data and integrating objective audio/video quality scores as rewards, offline RL can therefore facilitate the development of competitive bandwidth estimators for RTC.

cs.NI↗

arXiv2024

GRACE: Loss-Resilient Real-Time Video through Neural Codecs

In real-time video communication, retransmitting lost packets over high-latency networks is not viable due to strict latency requirements. To counter packet losses without retransmission, two primary strategies are employed -- encoder-based forward error correction (FEC) and decoder-based error concealment. The former encodes data with redundancy before transmission, yet determining the optimal redundancy level in advance proves challenging. The latter reconstructs video from partially received frames, but dividing a frame into independently coded partitions inherently compromises compression efficiency, and the lost information cannot be effectively recovered by the decoder without adapting the encoder. We present a loss-resilient real-time video system called GRACE, which preserves the user's quality of experience (QoE) across a wide range of packet losses through a new neural video codec. Central to GRACE's enhanced loss resilience is its joint training of the neural encoder and decoder under a spectrum of simulated packet losses. In lossless scenarios, GRACE achieves video quality on par with conventional codecs (e.g., H.265). As the loss rate escalates, GRACE exhibits a more graceful, less pronounced decline in quality, consistently outperforming other loss-resilient schemes. Through extensive evaluation on various videos and real network traces, we demonstrate that GRACE reduces undecodable frames by 95% and stall duration by 90% compared with FEC, while markedly boosting video quality over error concealment methods. In a user study with 240 crowdsourced participants and 960 subjective ratings, GRACE registers a 38% higher mean opinion score (MOS) than other baselines.

cs.MM↗

arXiv2022

Automatic Curriculum Generation for Learning Adaptation in Networking

As deep reinforcement learning (RL) showcases its strengths in networking and systems, its pitfalls also come to the public's attention--when trained to handle a wide range of network workloads and previously unseen deployment environments, RL policies often manifest suboptimal performance and poor generalizability. To tackle these problems, we present Genet, a new training framework for learning better RL-based network adaptation algorithms. Genet is built on the concept of curriculum learning, which has proved effective against similar issues in other domains where RL is extensively employed. At a high level, curriculum learning gradually presents more difficult environments to the training, rather than choosing them randomly, so that the current RL model can make meaningful progress in training. However, applying curriculum learning in networking is challenging because it remains unknown how to measure the "difficulty" of a network environment. Instead of relying on handcrafted heuristics to determine the environment's difficulty level, our insight is to utilize traditional rule-based (non-RL) baselines: If the current RL model performs significantly worse in a network environment than the baselines, then the model's potential to improve when further trained in this environment is substantial. Therefore, Genet automatically searches for the environments where the current model falls significantly behind a traditional baseline scheme and iteratively promotes these environments as the training progresses. Through evaluating Genet on three use cases--adaptive video streaming, congestion control, and load balancing, we show that Genet produces RL policies which outperform both regularly trained RL policies and traditional baselines in each context, not only under synthetic workloads but also in real environments.

cs.NI↗

arXiv2020

Implementing BOLA-BASIC on Puffer: Lessons for the use of SSIM in ABR logic

One ABR algorithm implemented on Puffer is BOLA-BASIC, the simplest variant of BOLA. BOLA finds wide use in industry, notably in the MPEG-DASH reference player used as the basis for video players at Akamai, BBC, Orange, and CBS. The overall goal of BOLA is to maximize each encoded chunk's video quality while minimizing rebuffering. To measure video quality, Puffer uses the structural similarity metric SSIM, whereas BOLA and other ABR algorithms like BBA, MPC, and Pensieve are more commonly implemented using bitrate (or a variant of bitrate). While bitrate is frequently used, BOLA allows the video provider to define its own proxy of video quality as the algorithm's "utility" function. However, using SSIM as utility proved surprisingly complex for BOLA-BASIC, despite the algorithm's simplicity. Given the rising popularity of SSIM and related quality metrics, we anticipate that a growing number of Puffer-like systems will face similar challenges. We hope developers of such systems find our experiences informative as they implement algorithms designed with bitrate-based utility in mind.

cs.NI↗

arXiv2019

Learning in situ: a randomized experiment in video streaming

We describe the results of a randomized controlled trial of video-streaming algorithms for bitrate selection and network prediction. Over the last eight months, we have streamed 14.2 years of video to 56,000 users across the Internet. Sessions are randomized in blinded fashion among algorithms, and client telemetry is recorded for analysis. We found that in this real-world setting, it is difficult for sophisticated or machine-learned control schemes to outperform a "simple" scheme (buffer-based control), notwithstanding good performance in network emulators or simulators. We performed a statistical analysis and found that the variability and heavy-tailed nature of network and algorithm behavior create hurdles for robust learned algorithms in this area. We developed an ABR algorithm that robustly outperforms other schemes in practice, by combining classical control with a learned network predictor, trained with supervised learning in situ on data from the real deployment environment. To support further investigation, we are publishing an archive of traces and results each day, and will open our ongoing study to the community. We welcome other researchers to use this platform to develop and validate new algorithms for bitrate selection, network prediction, and congestion control.

cs.NI↗