NSDI '25: 22nd USENIX Symposium on Networked Systems Design and Implementation: Full Schedule

8:00am EDT

Continental Breakfast

Tuesday April 29, 2025 8:00am - 9:00am EDT

Liberty Ballroom Foyer

Tuesday April 29, 2025 8:00am - 9:00am EDT
Liberty Ballroom Foyer

8:00am EDT

Badge Pickup

Tuesday April 29, 2025 8:00am - 5:00pm EDT

Liberty Ballroom Foyer

Tuesday April 29, 2025 8:00am - 5:00pm EDT
Liberty Ballroom Foyer

9:00am EDT

AutoCCL: Automated Collective Communication Tuning for Accelerating Distributed and Parallel DNN Training

Tuesday April 29, 2025 9:00am - 9:20am EDT

Liberty Ballroom

Guanbin Xu, Zhihao Le, Yinhe Chen, Zhiqi Lin, and Zewen Jin, University of Science and Technology of China; Youshan Miao, Microsoft Research; Cheng Li, University of Science and Technology of China; Anhui Province Key Laboratory of Biomedical Imaging and Intelligent Processing; Institute of Artificial Intelligence, Hefei Comprehensive National Science Center

The collective communication libraries are pivotal in optimizing the performance of distributed and parallel deep neural network (DNN) training. Most network optimizations are under the assumption that these libraries are well-tuned, ignoring their low-level parameter selection. In this paper, we present a novel automated tuning method AutoCCL that significantly improves communication performance without incurring additional costs. One of the primary challenges we tackle is the state explosion in searching for the optimal configuration. To overcome this, we decouple implementation-related parameters from those sensitive to the search space size and propose a divide-and-conquer algorithm, minimizing the requirement for exhaustive trials. We further propose an online tuning approach that accounts for communication-computation interference to enhance accuracy in finding optimal configurations, while hiding tuning overhead within early iterations of training jobs. We implement AutoCCL atop NCCL, a leading and widely-used communication library provided by NVIDIA. Our evaluation on both a 2-node cluster (16 A40 GPUs, intra-node NVLink, inter-node 2× 400Gbps InfiniBand) and a 4-node cluster (32 A40 GPUs, intra-node PCIe, inter-node 100Gbps InfiniBand) demonstrates that AutoCCL achieves 1.24-1.29× and 1.15-1.22× speedups on microbenchmarks compared to NCCL and another SOTA NCCL tuner, respectively, and up to 1.80× and 1.49× with concurrent computation. End-to-end evaluations on three large language models and one vision model show 1.07-1.32× improvements in periteration training time.

https://www.usenix.org/conference/nsdi25/presentation/xu-guanbin

Tuesday April 29, 2025 9:00am - 9:20am EDT
Liberty Ballroom

Track 1

9:00am EDT

Pineapple: Unifying Multi-Paxos and Atomic Shared Registers

Tuesday April 29, 2025 9:00am - 9:20am EDT

Independence Ballroom

Tigran Bantikyan, Northwestern; Jonathan Zarnstorff, unaffiliated; Te-Yen Chou, CMU; Lewis Tseng, UMass Lowell; Roberto Palmieri, Lehigh University

Linearizable storage systems reduce the complexity of developing correct large-scale customer-facing applications, in the presence of concurrent operations and failures. A common approach for providing linearizability is to use consensus to order operations invoked by applications. This paper explores designs that offload operations (from the consensus component) to improve overall performance.

This paper presents Pineapple, which uses logical timestamps to unify Multi-Paxos and atomic shared registers so that any node in the system can serve read and write operations. Compared to Multi-Paxos (or leader-based consensus), Pineapple reduces bottlenecks at the leader. Compared to Gryff, which unifies EPaxos and atomic shared registers, Pineapple has better performance because Pineapple has “non-blocking operation execution.”

Our evaluation shows that Pineapple improves both throughput and tail latency, compared to state-of-the-art systems (e.g., Gryff, Multi-Paxos, EPaxos), in both wide-area networks and local-area networks. We also integrate Pineapple with etcd. In a balanced workload, Pineapple reduces median latency by more than 50%, compared to the original system that uses an optimized version of Raft.

https://www.usenix.org/conference/nsdi25/presentation/bantikyan

Tuesday April 29, 2025 9:00am - 9:20am EDT
Independence Ballroom

Track 2

9:20am EDT

OptiReduce: Resilient and Tail-Optimal AllReduce for Distributed Deep Learning in the Cloud

Tuesday April 29, 2025 9:20am - 9:40am EDT

Liberty Ballroom

Ertza Warraich, Purdue University; Omer Shabtai and Khalid Manaa, Nvidia; Shay Vargaftik, VMware Research; Yonatan Piasetzky and Matty Kadosh, Nvidia; Lalith Suresh, Feldera; Muhammad Shahbaz, University of Michigan

We present OptiReduce, a new collective-communication system for the cloud with bounded, predictable completion times for deep-learning jobs in the presence of varying computation (stragglers) and communication (congestion and gradient drops) variabilities. OptiReduce exploits the inherent resiliency and the stochastic nature of distributed deep-learning (DDL) training and fine-tuning to work with approximated (or lost) gradients—providing an efficient balance between (tail) performance and the resulting accuracy of the trained models.

Exploiting this domain-specific characteristic of DDL, OptiReduce introduces (1) mechanisms (e.g., unreliable bounded transport with adaptive timeout) to improve the DDL jobs’ tail execution time, and (2) strategies (e.g., Transpose AllReduce and Hadamard Transform) to mitigate the impact of gradient drops on model accuracy. Our evaluation shows that OptiReduce achieves 70% and 30% faster time-to-accuracy (TTA), on average, when operating in shared, cloud environments (e.g., CloudLab) compared to Gloo and NCCL, respectively.

https://www.usenix.org/conference/nsdi25/presentation/warraich

Tuesday April 29, 2025 9:20am - 9:40am EDT
Liberty Ballroom

Track 1

9:20am EDT

Ladder: A Convergence-based Structured DAG Blockchain for High Throughput and Low Latency

Tuesday April 29, 2025 9:20am - 9:40am EDT

Independence Ballroom

Dengcheng Hu, Jianrong Wang, Xiulong Liu, and Hao Xu, Tianjin University; Xujing Wu, Jd.Com, Inc; Muhammad Shahzad, North Carolina State University; Guyue Liu, Peking University; Keqiu Li, Tianjin University

Recent literature proposes the use of Directed Acyclic Graphs (DAG) to enhance blockchain performance. However, current block-DAG designs face three important limitations when fully utilizing parallel block processing: high computational overhead due to costly block sorting, complex transaction confirmation process, and vulnerability to balance attacks when determining the pivot chain. To this end, we propose Ladder, a structured twin-chain DAG blockchain with a convergence mechanism that efficiently optimizes parallel block processing strategy and enhances overall performance and security. In each round, a designated convergence node generates a lower-chain block, sorting the forked blocks from the upper-chain, reducing computational overhead and simplifying transaction confirmation.To counter potential adversarial disruptions, a dynamic committee is selected to generate special blocks when faulty blocks are detected. We implemented and evaluated Ladder in a distributed network environment against several state-of-the-art methods. Our results show that Ladder achieves a 59.6% increase in throughput and a 20.9% reduction in latency.

https://www.usenix.org/conference/nsdi25/presentation/hu

Tuesday April 29, 2025 9:20am - 9:40am EDT
Independence Ballroom

Track 2

9:40am EDT

Efficient Direct-Connect Topologies for Collective Communications

Tuesday April 29, 2025 9:40am - 10:00am EDT

Liberty Ballroom

Liangyu Zhao, University of Washington; Siddharth Pal, Raytheon BBN Technologies; Tapan Chugh, University of Washington; Weiyang Wang, MIT CSAIL; Jason Fantl, Prithwish Basu, and Joud Khoury, Raytheon BBN Technologies; Arvind Krishnamurthy, University of Washington

We consider the problem of distilling efficient network topologies for collective communications. We provide an algorithmic framework for constructing direct-connect topologies optimized for the latency vs. bandwidth trade-off associated with the workload. Our approach synthesizes many different topologies and communication schedules for a given cluster size and degree, then identifies the best option for a given workload. Our algorithms start from small, optimal base topologies and associated schedules, using techniques that can be iteratively applied to derive much larger topologies and schedules. Additionally, we incorporate well-studied large-scale graph topologies into our algorithmic framework by producing efficient communication schedules for them using a novel polynomial-time algorithm. Our evaluation uses multiple testbeds and large-scale simulations to demonstrate significant performance benefits from our derived topologies and schedules.

https://www.usenix.org/conference/nsdi25/presentation/zhao-liangyu

Tuesday April 29, 2025 9:40am - 10:00am EDT
Liberty Ballroom

Track 1

9:40am EDT

Vegeta: Enabling Parallel Smart Contract Execution in Leaderless Blockchains

Tuesday April 29, 2025 9:40am - 10:00am EDT

Independence Ballroom

Tianjing Xu and Yongqi Zhong, Shanghai Jiao Tong University; Yiming Zhang, Shanghai Jiao Tong University and Shanghai Key Laboratory of Trusted Data Circulation, Governance and Web3; Ruofan Xiong, Xiamen University; Jingjing Zhang, Fudan University; Guangtao Xue and Shengyun Liu, Shanghai Jiao Tong University and Shanghai Key Laboratory of Trusted Data Circulation, Governance and Web3

Consensus and smart contract execution play complementary roles in blockchain systems. Leaderless consensus, as a promising direction in the blockchain context, can better utilize the resources of each node and/or avoid incurring the extra burden of timing assumptions. As modern Byzantine-Fault Tolerant (BFT) consensus protocols can order several hundred thousand transactions per second, contract execution is becoming the performance bottleneck. Adding concurrency to contract execution is a natural way to boost its performance, but none of the existing frameworks is a perfect fit for leaderless consensus.

We propose speculate-order-replay, a generic framework tailored to leaderless consensus protocols. Our framework allows each proposer to (pre-)process transactions prior to consensus, better utilizing its computing resources. We instantiate the framework with a concrete concurrency control protocol Vegeta. Vegeta speculatively executes a series of transactions and analyzes their dependencies before consensus, and later deterministically replays the schedule. We ran experiments under the real-world Ethereum workload on 16vCPU virtual machines. Our evaluation results show that Vegeta achieved up to 7.8× speedup compared to serial execution. When deployed on top of a leaderless consensus protocol with 10 nodes, Vegeta still achieved 6.9× speedup.

https://www.usenix.org/conference/nsdi25/presentation/xu-tianjing

Tuesday April 29, 2025 9:40am - 10:00am EDT
Independence Ballroom

Track 2

10:00am EDT

SuperServe: Fine-Grained Inference Serving for Unpredictable Workloads

Tuesday April 29, 2025 10:00am - 10:20am EDT

Liberty Ballroom

Alind Khare and Dhruv Garg, Georgia Institute of Technology; Sukrit Kalra, UC Berkeley; Snigdha Grandhi, Adobe; Ion Stoica, UC Berkeley; Alexey Tumanov, Georgia Institute of Technology

The increasing deployment of ML models on the critical path of production applications requires ML inference serving systems to serve these models under unpredictable and bursty request arrival rates. Serving many models under such conditions requires a careful balance between each application’s latency and accuracy requirements and the overall efficiency of utilization of scarce resources. Faced with this tension, state-of-the-art systems either choose a single model representing a static point in the latency-accuracy tradeoff space to serve all requests or incur latency target violations by loading specific models on the critical path of request serving. Our work instead resolves this tension through a resource-efficient serving of the entire range of models spanning the latency-accuracy tradeoff space. Our novel mechanism, SubNetAct, achieves this by carefully inserting specialized control-flow operators in pre-trained, weight-shared super-networks. These operators enable SubNetAct to dynamically route a request through the network to actuate a specific model that meets the request’s latency and accuracy target. Thus, SubNetAct can serve a vastly higher number of models than prior systems while requiring upto 2.6× lower memory. More crucially, SubNetAct’s near-instantaneous actuation of a wide-range of models unlocks the design space of fine-grained, reactive scheduling policies. We design one such extremely effective policy, SlackFit, and instantiate both SubNetAct and SlackFit in a real system, SuperServe. On real-world traces derived from a Microsoft workload, SuperServe achieves 4.67% higher accuracy for the same latency targets and 2.85× higher latency target attainment for the same accuracy.

https://www.usenix.org/conference/nsdi25/presentation/khare

Tuesday April 29, 2025 10:00am - 10:20am EDT
Liberty Ballroom

Track 1

10:00am EDT

Shoal++: High Throughput DAG BFT Can Be Fast and Robust!

Tuesday April 29, 2025 10:00am - 10:20am EDT

Independence Ballroom

Balaji Arun and Zekun Li, Aptos Labs; Florian Suri-Payer, Cornell University; Sourav Das, UIUC; Alexander Spiegelman, Aptos Labs

Today's practical partially synchronous Byzantine Fault Tolerant consensus protocols trade off low latency and high throughput. On the one end, traditional BFT protocols such as PBFT and its derivatives optimize for latency. They require, in fault-free executions, only 3 message delays to commit, the optimum for BFT consensus. However, this class of protocols typically relies on a single leader, hampering throughput scalability. On the other end, a new class of so-called DAG-BFT protocols demonstrates how to achieve highly scalable throughput by separating data dissemination from consensus, and using every replica as proposer. Unfortunately, existing DAG-BFT protocols pay a steep latency premium, requiring on average 10.5 message delays to commit transactions.

This work aims to soften this tension, and proposes Shoal++, a novel DAG-based BFT consensus system that offers the throughput of DAGs while reducing end-to-end consensus commit latency to an average of 4.5 message delays. Our empirical findings are encouraging, showing that Shoal++ achieves throughput comparable to state-of-the-art DAG BFT solutions while reducing latency by up to 60%, even under less favorable network and failure conditions.

https://www.usenix.org/conference/nsdi25/presentation/arun

Tuesday April 29, 2025 10:00am - 10:20am EDT
Independence Ballroom

Track 2

10:20am EDT

Coffee and Tea Break

Tuesday April 29, 2025 10:20am - 10:50am EDT

Liberty Ballroom Foyer

Tuesday April 29, 2025 10:20am - 10:50am EDT
Liberty Ballroom Foyer

10:50am EDT

Learning Production-Optimized Congestion Control Selection for Alibaba Cloud CDN

Tuesday April 29, 2025 10:50am - 11:10am EDT

Liberty Ballroom

Xuan Zeng, Alibaba Cloud; Haoran Xu, Sun Yat-sen University; Chen Chen and Xumiao Zhang, Alibaba Cloud; Xiaoxi Zhang and Xu Chen, Sun Yat-sen University; Guihai Chen, Nanjing University; Yubing Qiu, Yiping Zhang, Chong Hao, and Ennan Zhai, Alibaba Cloud

Today's content delivery networks (CDNs) typically use static congestion control (CC) configurations, yet the diverse network environments preclude a universally optimal CC for all geographical regions, as evidenced by our extensive measurements. Current CC algorithms, limited by narrow applicability or high maintenance costs, struggle in large-scale CDNs. This work introduces AliCCS, the first CC Selection (CCS) approach tailored for production CDN, integrating fine-grained domain knowledge for learning to choose the best CC from existing, well-established ones. Through an over-one-year real-world deployment in Alibaba Cloud CDN, AliCCS has enhanced the Quality-of-Experience (QoE) by up to 9.31%, surpassing the competitive margin in the CDN market, and significantly reduced the retransmission rate by 25.51% to 174.36% across all provinces of China, leading to cost savings over 10 million US dollars. We also share key insights and experiences from deploying AliCCS at scale, highlighting traffic patterns in Alibaba Cloud CDN.

https://www.usenix.org/conference/nsdi25/presentation/zeng

Tuesday April 29, 2025 10:50am - 11:10am EDT
Liberty Ballroom

Track 1

10:50am EDT

HA/TCP: A Reliable and Scalable Framework for TCP Network Functions

Tuesday April 29, 2025 10:50am - 11:10am EDT

Independence Ballroom

Haoyu Gu, Ali José Mashtizadeh, and Bernard Wong, University of Waterloo

Layer 7 network functions (NFs) are a critical piece of modern network infrastructure. As a result, the scalability and reliability of these NFs are important but challenging because of the complexity of layer 7 NFs. This paper presents HA/TCP, a framework that enables migration and failover of layer 7 NFs. HA/TCP uses a novel replication mechanism to synchronize the state between replicas with low overhead, enabling seamless migration and failover of TCP connections. HA/TCP encapsulates the implementation details into our replicated socket interface to allow developers to easily add high availability to their layer 7 NFs such as WAN accelerators, load balancers, and proxies. Our benchmarks show that HA/TCP provides reliability for a 100 Gbps NF with as little as 0.2% decrease in client throughput. HA/TCP transparently migrates a connection between replicas in 38 µs, including the network latency. We provide reliability to a SOCKS proxy and a WAN accelerator with less than 2% decrease in throughput and a modest increase in CPU usage.

https://www.usenix.org/conference/nsdi25/presentation/gu

Tuesday April 29, 2025 10:50am - 11:10am EDT
Independence Ballroom

Track 2

11:10am EDT

GPU-Disaggregated Serving for Deep Learning Recommendation Models at Scale

Tuesday April 29, 2025 11:10am - 11:30am EDT

Liberty Ballroom

Lingyun Yang, Hong Kong University of Science and Technology; Yongchen Wang and Yinghao Yu, Alibaba Group; Qizhen Weng, Hong Kong University of Science and Technology; Jianbo Dong, Kan Liu, Chi Zhang, Yanyi Zi, Hao Li, Zechao Zhang, Nan Wang, Yu Dong, Menglei Zheng, Lanlan Xi, Xiaowei Lu, Liang Ye, Guodong Yang, Binzhang Fu, Tao Lan, Liping Zhang, and Lin Qu, Alibaba Group; Wei Wang, Hong Kong University of Science and Technology

Online recommender systems use deep learning recommendation models (DLRMs) to provide accurate, personalized recommendations to improve customer experience. However, efficiently provisioning DLRM services at scale is challenging. DLRMs exhibit distinct resource usage patterns: they require a large number of CPU cores and a tremendous amount of memory, but only a small number of GPUs. Running them in multi-GPU servers quickly exhausts the servers' CPU and memory resources, leaving a large number of unallocated GPUs stranded, unable to utilize by other tasks.

This paper describes Prism, a production DLRM serving system that eliminates GPU fragmentation by means of resource disaggregation. In Prism, a fleet of CPU nodes (CNs) interconnect with a cluster of heterogeneous GPU nodes (HNs) through RDMA, leading to two disaggregated resource pools that can independently scale. Prism automatically divides DLRMs into CPU- and GPU-intensive subgraphs and schedules them on CNs and HNs for disaggregated serving. Prism employs various techniques to minimize the latency overhead caused by disaggregation, including optimal graph partitioning, topology-aware resource management, and SLO-aware communication scheduling. Evaluations show that Prism effectively reduces CPU and GPU fragmentation by 53% and 27% in a crowded GPU cluster. During seasonal promotion events, it efficiently enables capacity loaning from training clusters, saving over 90% of GPUs. Prism has been deployed in production clusters for over two years and now runs on over 10k GPUs.

https://www.usenix.org/conference/nsdi25/presentation/yang

Tuesday April 29, 2025 11:10am - 11:30am EDT
Liberty Ballroom

Track 1

11:10am EDT

High-level Programming for Application Networks

Tuesday April 29, 2025 11:10am - 11:30am EDT

Independence Ballroom

Xiangfeng Zhu, Yuyao Wang, Banruo Liu, Yongtong Wu, and Nikola Bojanic, University of Washington; Jingrong Chen, Duke University; Gilbert Louis Bernstein and Arvind Krishnamurthy, University of Washington; Sam Kumar, University of Washington and UCLA; Ratul Mahajan, University of Washington; Danyang Zhuo, Duke University

Application networks facilitate communication between the microservices of cloud applications. They are built today using service meshes with low-level specifications that make it difficult to express application-specific functionality (e.g., access control based on RPC fields), and they can more than double the RPC latency. We develop AppNet, a framework that makes it easy to build expressive and high-performance application networks. Developers specify rich RPC processing in a high-level language with generalized match-action rules and built-in state management. We compile the specifications to high-performance code after optimizing where (e.g., client, server) and how (e.g., RPC library, proxy) each RPC processing element runs. The optimization uses symbolic abstraction and execution to judge if different runtime configurations of possibly-stateful RPC processing elements are semantically equivalent for arbitrary RPC streams. Our experiments show that AppNet can express common application network function in only 7-28 lines of code. Its optimizations lower RPC processing latency by up to 82%.

https://www.usenix.org/conference/nsdi25/presentation/zhu

Tuesday April 29, 2025 11:10am - 11:30am EDT
Independence Ballroom

Track 2

11:30am EDT

Evolution of Aegis: Fault Diagnosis for AI Model Training Service in Production

Tuesday April 29, 2025 11:30am - 11:50am EDT

Liberty Ballroom

Jianbo Dong, Kun Qian, Pengcheng Zhang, Zhilong Zheng, Liang Chen, Fei Feng, Yichi Xu, Yikai Zhu, Gang Lu, Xue Li, Zhihui Ren, Zhicheng Wang, Bin Luo, Peng Zhang, Yang Liu, Yanqing Chen, Yu Guan, Weicheng Wang, Chaojie Yang, Yang Zhang, Man Yuan, Hanyu Zhao, Yong Li, Zihan Zhao, Shan Li, Xianlong Zeng, Zhiping Yao, Binzhang Fu, Ennan Zhai, Wei Lin, Chao Wang, and Dennis Cai, Alibaba Cloud

Despite the success of diagnosis systems in traditional cloud computing, these systems are not suitable for pinpointing faults in AI model training cloud scenarios due to the differences in computing paradigms between traditional cloud computing and model training. As one of the largest cloud providers, we present Aegis, a fault diagnosis system specifically designed for AI model training service. We share our experience in the motivation, design, and evolution of Aegis. Keeping easy-to-deploy as the primary principle, Aegis Phase- 1 started by enhancing existing general-purpose diagnosis systems. After several months of evolution, Aegis Phase-2 cogitatively chose to customize the collective communication library for sophisticated failure localization in runtime without modifying customer code. Besides the failure localization, we further equipped Aegis with the capabilities on handling performance degradation and failure checking before delivery. Aegis has been deployed in our production training cloud service for one year. Aegis decreases more than 97% of the idle time wasted by diagnosis, 84% of the training task restart count and 71% of the performance degradation.

https://www.usenix.org/conference/nsdi25/presentation/dong

Tuesday April 29, 2025 11:30am - 11:50am EDT
Liberty Ballroom

Track 1

11:30am EDT

State-Compute Replication: Parallelizing High-Speed Stateful Packet Processing

Tuesday April 29, 2025 11:30am - 11:50am EDT

Independence Ballroom

Qiongwen Xu, Rutgers University; Sebastiano Miano, Politecnico di Milano; Xiangyu Gao and Tao Wang, New York University; Adithya Murugadass and Songyuan Zhang, Rutgers University; Anirudh Sivaraman, New York University; Gianni Antichi, Queen Mary University of London and Politecnico di Milano; Srinivas Narayana, Rutgers University

With the slowdown of Moore’s law, CPU-oriented packet processing in software will be significantly outpaced by emerging line speeds of network interface cards (NICs). Single-core packet-processing throughput has saturated.

We consider the problem of high-speed packet processing with multiple CPU cores. The key challenge is state—memory that multiple packets must read and update. The prevailing method to scale throughput with multiple cores involves state sharding, processing all packets that update the same state, e.g., flow, at the same core. However, given the skewed nature of realistic flow size distributions, this method is untenable, since total throughput is limited by single-core performance.

This paper introduces state-compute replication, a principle to scale the throughput of a single stateful flow across multiple cores using replication. Our design leverages a packet history sequencer running on a NIC or top-of-the-rack switch to enable multiple cores to update state without explicit synchronization. Our experiments with realistic data center and wide-area Internet traces show that state-compute replication can scale total packet-processing throughput linearly with cores, independent of flow size distributions, across a range of realistic packet-processing programs.

https://www.usenix.org/conference/nsdi25/presentation/xu-qiongwen

Tuesday April 29, 2025 11:30am - 11:50am EDT
Independence Ballroom

Track 2

11:50am EDT

PAPAYA Federated Analytics Stack: Engineering Privacy, Scalability and Practicality

Tuesday April 29, 2025 11:50am - 12:10pm EDT

Liberty Ballroom

Harish Srinivas, Graham Cormode, Mehrdad Honarkhah, Samuel Lurye, Jonathan Hehir, Lunwen He, George Hong, Ahmed Magdy, Dzmitry Huba, Kaikai Wang, Shen Guo, and Shoubhik Bhattacharya, Meta

Cross-device Federated Analytics (FA) is a distributed computation paradigm designed to answer analytics queries about and derive insights from data held locally on users’ devices. On-device computations combined with other privacy and security measures ensure that only minimal data is transmitted off-device, achieving a high standard of data protection. Despite FA’s broad adoption, the applicability of existing FA systems is limited by compromised accuracy; lack of flexibility for data analytics; and an inability to scale effectively. In this paper, we describe our approach to combine privacy, scalability, and practicality to build a system that overcomes these limitations. The PAPAYA system at Meta system leverages trusted execution environments (TEEs) and optimizes the use of on-device computing resources to facilitate federated data processing across large fleets of devices, while ensuring robust, defensible, and verifiable privacy safeguards. We focus on federated analytics (statistics and monitoring), in contrast to systems for federated learning (ML workloads), and we flag the key differences.

https://www.usenix.org/conference/nsdi25/presentation/srinivas

Tuesday April 29, 2025 11:50am - 12:10pm EDT
Liberty Ballroom

Track 1

11:50am EDT

MTP: Transport for In-Network Computing

Tuesday April 29, 2025 11:50am - 12:10pm EDT

Independence Ballroom

Tao Ji, UT Austin; Rohan Vardekar and Balajee Vamanan, University of Illinois Chicago; Brent E. Stephens, Google and University of Utah; Aditya Akella, UT Austin

In-network computing (INC) is being increasingly adopted to accelerate applications by offloading part of the applications’ computation to network devices. Such application-specific (L7) offloads have several attributes that the transport protocol must work with — they may mutate, intercept, reorder and
delay application messages that span multiple packets. At the same time the transport must also work with the buffering and computation constraints of network devices hosting the L7 offloads. Existing transports and alternative approaches fall short in these regards. Therefore, we present MTP, the first transport to natively support INC. MTP is built around two major components: 1) a novel message-oriented reliability protocol and 2) a resource-specific congestion control framework. We implement a full-fledged prototype of MTP based on DPDK. We show the efficacy of MTP in a testbed with a real INC application as well as with comprehensive microbenchmarks and large-scale simulations.

https://www.usenix.org/conference/nsdi25/presentation/ji

Tuesday April 29, 2025 11:50am - 12:10pm EDT
Independence Ballroom

Track 2

12:10pm EDT

Lunch (on your own)

Tuesday April 29, 2025 12:10pm - 2:00pm EDT

Tuesday April 29, 2025 12:10pm - 2:00pm EDT
-

2:00pm EDT

ONCache: A Cache-Based Low-Overhead Container Overlay Network

Tuesday April 29, 2025 2:00pm - 2:20pm EDT

Liberty Ballroom

Shengkai Lin, Shizhen Zhao, Peirui Cao, and Xinchi Han, Shanghai Jiao Tong University; Quan Tian, Wenfeng Liu, Qi Wu, and Donghai Han, Broadcom; Xinbing Wang, Shanghai Jiao Tong University
Recent years have witnessed a widespread adoption of containers. While containers simplify and accelerate application development, existing container network technologies either incur significant overhead, which hurts performance for distributed applications, or lose flexibility or compatibility, which hinders the widespread deployment in production.
We carefully analyze the kernel data path of an overlay network, quantifying the time consumed by each segment of the data path and identifying the extra overhead in an overlay network compared to bare metal. We observe that this extra overhead generates repetitive results among packets, which inspires us to introduce caches within an overlay network.
We design and implement ONCache (Overlay Network Cache), a cache-based container overlay network, to eliminate the extra overhead while maintaining flexibility and compatibility. We implement ONCache using the extended Berkeley Packet Filter (eBPF) with only 524 lines of code, and integrate it as a plugin of Antrea. With ONCache, containers attain networking performance akin to that of bare metal. Compared to the standard overlay networks, ONCache improves throughput and request-response transaction rate by 12% and 36% for TCP (20% and 34% for UDP), respectively, while significantly reducing per-packet CPU overhead. Popular distributed applications also benefit from ONCache.
https://www.usenix.org/conference/nsdi25/presentation/lin-shengkai

Tuesday April 29, 2025 2:00pm - 2:20pm EDT
Liberty Ballroom

Track 1

2:00pm EDT

Mitigating Scalability Walls of RDMA-based Container Networks

Tuesday April 29, 2025 2:00pm - 2:20pm EDT

Independence Ballroom

Wei Liu, Tsinghua University and Alibaba Cloud; Kun Qian, Alibaba Cloud; Zhenhua Li, Tsinghua University; Feng Qian, University of Southern California; Tianyin Xu, UIUC; Yunhao Liu, Tsinghua University; Yu Guan, Shuhong Zhu, Hongfei Xu, Lanlan Xi, Chao Qin, and Ennan Zhai, Alibaba Cloud

As a state-of-the-art technique, RDMA-offloaded container networks (RCNs) can provide high-performance data communications among containers. Nevertheless, this seems to be subject to the RCN scale—when there are millions of containers simultaneously running in a data center, the performance decreases sharply and unexpectedly. In particular, we observe that most performance issues are related to RDMA NICs (RNICs), whose design and implementation defects might constitute the "scalability wall" of the RCN. To validate the conjecture, however, we are challenged by the limited visibility into the internals of today's RNICs. To address the dilemma, a more pragmatic approach is to infer the most likely causes of the performance issues according to the common abstractions of an RNIC's components and functionalities.

Specifically, we conduct combinatorial causal testing to efficiently reason about an RNIC's architecture model, effectively approximate its performance model, and thereby proactively optimize the NF (network function) offloading schedule. We embody the design into a practical system dubbed ScalaCN. Evaluation on production workloads shows that the end-to-end network bandwidth increases by 1.4× and the packet forwarding latency decreases by 31%, after resolving 82% of the causes inferred by ScalaCN. We report the performance issues of RNICs and the most likely causes to relevant vendors, all of which have been encouragingly confirmed; we are now closely working with the vendors to fix them.

https://www.usenix.org/conference/nsdi25/presentation/liu-wei

Tuesday April 29, 2025 2:00pm - 2:20pm EDT
Independence Ballroom

Track 2

2:20pm EDT

GREEN: Carbon-efficient Resource Scheduling for Machine Learning Clusters

Tuesday April 29, 2025 2:20pm - 2:40pm EDT

Liberty Ballroom

Kaiqiang Xu and Decang Sun, iSING Lab, Hong Kong University of Science and Technology; Han Tian, USTC; Junxue Zhang and Kai Chen, iSING Lab, Hong Kong University of Science and Technology

This paper explores the problem of scheduling machine Learning (ML) jobs while also taking into account the reduction of carbon emissions in the cluster. Traditional cluster schedulers for ML jobs mainly focus on optimizing job completion time~(JCT), but do not consider the environmental impact of their decisions, resulting in a suboptimal carbon footprint. To address this issue, we propose GREEN, an ML cluster scheduler that is both time-efficient and carbon-efficient. At its core, GREEN uses a unique carbon-aware scheduling algorithm that reduces carbon footprint with minimized impact on JCT.
Additionally, it leverages the temporal flexibility of ML jobs to reduce carbon emissions by shifting workloads to less carbon-intensive times, while still maintaining overall daily capacity. Our experiments using real ML jobs workload demonstrate that GREEN can achieve up to 41.2% reduction in cluster-wide carbon footprint and 12% reduction in peak power consumption, while incurring 3.6%-5.9% time efficiency tradeoff compared to existing methods.

https://www.usenix.org/conference/nsdi25/presentation/xu-kaiqiang

Tuesday April 29, 2025 2:20pm - 2:40pm EDT
Liberty Ballroom

Track 1

2:20pm EDT

Eden: Developer-Friendly Application-Integrated Far Memory

Tuesday April 29, 2025 2:20pm - 2:40pm EDT

Independence Ballroom

Anil Yelam, Stewart Grant, and Saarth Deshpande, UC San Diego; Nadav Amit, Technion, Israel Institute of Technology; Radhika Niranjan Mysore, VMware Research Group; Amy Ousterhout, UC San Diego; Marcos K. Aguilera, VMware Research Group; Alex C. Snoeren, UC San Diego

Far memory systems are a promising way to address the resource stranding problem in datacenters. Far memory systems broadly fall into two categories. On one hand, paging-based systems use hardware guards at the granularity of pages to intercept remote accesses, which require no application changes but incur significant faulting overhead. On the other hand, app-integrated systems use software guards on data objects and apply application-specific optimizations to avoid faulting overheads, but these systems require significant application redesign and/or suffer from overhead on local accesses. We propose Eden, a new approach to far memory that combines hardware guards with a small number of software guards in the form of programmer annotations, to achieve performance similar to app-integrated systems with minimal developer effort. Eden is based on the insight that applications generate most of their page faults at a small number of code locations, and those locations are easy to find programmatically. By adding hints to such locations, Eden can notify the pager about upcoming memory accesses to customize read-ahead and memory reclamation. We show that Eden achieves 19.4–178% higher performance than Fastswap for memory-intensive applications including DataFrame and memcached. Eden achieves performance comparable to AIFM with almost 100× fewer code changes.

https://www.usenix.org/conference/nsdi25/presentation/yelam

Tuesday April 29, 2025 2:20pm - 2:40pm EDT
Independence Ballroom

Track 2

2:40pm EDT

The Benefits and Limitations of User Interrupts for Preemptive Userspace Scheduling

Tuesday April 29, 2025 2:40pm - 3:00pm EDT

Liberty Ballroom

Linsong Guo, Danial Zuberi, Tal Garfinkel, and Amy Ousterhout, UC San Diego

Preemptive scheduling promises to mitigate head-of-line blocking and enable flexible scheduling while retaining a simple programming model. Despite this, preemption is underutilized in server-side software today. Instead, high-performance datacenter systems and language runtimes often rely on cooperative concurrency, or else use preemption only at very coarse timescales, limiting its effectiveness. A key reason that preemption is underutilized today is that existing preemption mechanisms have high and unpredictable overheads.

Intel recently introduced support for user interrupts, a new feature that offers an opportunity to change this. By enabling interrupts to be sent and received entirely in user space, user interrupts can significantly lower the overhead of preemption. In this paper, we shed light on how user interrupts impact the landscape of preemption mechanisms. We build two user-level schedulers that leverage user interrupts for low-overhead preemption. We find that user interrupts are not a panacea. For example, they provide limited benefits when other software layers constrain the kinds of scheduling policies that can be used. Still, user interrupts can match or exceed the performance of existing mechanisms for all but the highest preemption rates, while achieving much more consistent overheads and retaining a user friendly programming model.

https://www.usenix.org/conference/nsdi25/presentation/guo

Tuesday April 29, 2025 2:40pm - 3:00pm EDT
Liberty Ballroom

Track 1

2:40pm EDT

Achieving Wire-Latency Storage Systems by Exploiting Hardware ACKs

Tuesday April 29, 2025 2:40pm - 3:00pm EDT

Independence Ballroom

Qing Wang, Jiwu Shu, Jing Wang, and Yuhao Zhang, Tsinghua University

We present Juneberry, a low-latency communication framework for storage systems. Different from existing RPC frameworks, Juneberry provides a fast path for storage requests: they can be committed with a single round trip and server CPU bypass, thus delivering extremely low latency; the execution of these requests is performed asynchronously on the server CPU. Juneberry achieves it by relying on our proposed Ordered Queue abstraction, which exploits NICs’ hardware ACKs as commit signals of requests while ensuring linearizability of the whole system. Juneberry also supports durability by placing requests in persistent memory (PM). We implement Juneberry using commodity RDMA NICs and integrate it into two storage systems: Memcached (a widely used in-memory caching system) and PMemKV (a PM-based persistent key-value store). Evaluation shows that compared with RPC, Juneberry can significantly lower their latency under write-intensive workloads.

https://www.usenix.org/conference/nsdi25/presentation/wang-qing

Tuesday April 29, 2025 2:40pm - 3:00pm EDT
Independence Ballroom

Track 2

3:00pm EDT

Securing Public Cloud Networks with Efficient Role-based Micro-Segmentation

Tuesday April 29, 2025 3:00pm - 3:20pm EDT

Liberty Ballroom

Sathiya Kumaran Mani and Kevin Hsieh, Microsoft; Santiago Segarra, Rice University; Ranveer Chandra, Microsoft; Yajie Zhou, University of Maryland; Srikanth Kandula, Microsoft

Securing network traffic within data centers is a critical and daunting challenge due to the increasing complexity and scale of modern public clouds. Micro-segmentation offers a promising solution by implementing fine-grained, workload-specific network security policies to mitigate potential attacks. However, the dynamic nature and large scale of deployments present significant obstacles in crafting precise security policies, limiting the practicality of this approach. To address these challenges, we introduce a novel system that efficiently processes vast volumes of network-flow logs and effectively infers the roles of network endpoints. Our method integrates domain knowledge and communication patterns in a principled manner, facilitating the creation of micro-segmentation policies at a large scale. Evaluations with real-world deployment demonstrate that our solution significantly surpasses existing algorithms in role inference accuracy. We implement our solution as an end-to-end system, and we show that our system is up to 21.5× more cost-efficient than Apache Flink, a widely used open-source stream processing system.

https://www.usenix.org/conference/nsdi25/presentation/mani

Tuesday April 29, 2025 3:00pm - 3:20pm EDT
Liberty Ballroom

Track 1

3:00pm EDT

ODRP: On-Demand Remote Paging with Programmable RDMA

Tuesday April 29, 2025 3:00pm - 3:20pm EDT

Independence Ballroom

Zixuan Wang, Xingda Wei, Jinyu Gu, Hongrui Xie, Rong Chen, and Haibo Chen, Institute of Parallel and Distributed Systems, SEIEE, Shanghai Jiao Tong University

Memory disaggregation with OS swapping is becoming popular for next-generation datacenters. RDMA is a promising technique for achieving this. However, RDMA does not support dynamic memory management in the data path. Current systems rely on RDMA’s control path operations, which are designed for coarse-grained memory management. This results in a trade-off between performance and memory utilization and also requires significant CPU usage, which is a limited resource on memory nodes.

This paper introduces On-Demand Remote Paging, the first system that smartly chains native RDMA data path primitives to offload all memory access and management operations onto the RDMA-capable NIC (RNIC). However, efficiently implementing these operations is challenging due to the limited capability of RNIC. ODRP leverages the semantics of OS swapping and adopts a client-assisted principle to address the efficiency and functionality challenges. Compared to the state-of-the-art system, ODRP can achieve significantly better memory utilization, no CPU usage while introducing only a 0.8% to 14.6% performance overhead in real-world applications.

https://www.usenix.org/conference/nsdi25/presentation/wang-zixuan

Tuesday April 29, 2025 3:00pm - 3:20pm EDT
Independence Ballroom

Track 2

3:20pm EDT

Coffee and Tea Break

Tuesday April 29, 2025 3:20pm - 3:50pm EDT

Liberty Ballroom Foyer

Tuesday April 29, 2025 3:20pm - 3:50pm EDT
Liberty Ballroom Foyer

3:50pm EDT

Understanding and Profiling NVMe-over-TCP Using ntprof

Tuesday April 29, 2025 3:50pm - 4:10pm EDT

Liberty Ballroom

Yuyuan Kang and Ming Liu, University of Wisconsin-Madison

NVMe-over-TCP (NVMe/TCP) is an emerging remote storage protocol, increasingly adopted in enterprises and clouds. It establishes a high-performance reliable data channel between clients and storage targets to deliver block I/Os. Understanding and analyzing the protocol execution details and how well storage workloads run atop are pivotal for system developers and infrastructure engineers. However, our community lacks such a profiling utility, whereas existing solutions are ad-hoc, tedious, and heuristic-driven. Realizing it is challenging due to the unpredictable I/O workload profile, intricate system layer interaction, and deep execution pipeline.

This paper presents ntprof, a systematic, informative, and lightweight NVMe/TCP profiler. Our key idea is to view the NVMe/TCP storage substrate as a lossless switched network and apply network monitoring techniques. We model each on-path system module as a software switch, equip it with a programmable profiling agent on the data plane, and develop a proactive query interface for statistics collection and analysis. ntprof, comprising a kernel module and a user-space utility, allows developers to define various profiling tasks, incurs marginal overhead when co-locating with applications, and generates performance reports based on prescribed specifications. We build ntprof atop Linux kernel 5.15.143 and apply it in six cases, i.e., end-to-end latency breakdown, interference analysis, SW/HW bottleneck localization, and application performance diagnostic. ntprof is available at https://github.com/netlab-wisconsin/ntprof.

https://www.usenix.org/conference/nsdi25/presentation/kang

Tuesday April 29, 2025 3:50pm - 4:10pm EDT
Liberty Ballroom

Track 1

3:50pm EDT

CellReplay: Towards accurate record-and-replay for cellular networks

Tuesday April 29, 2025 3:50pm - 4:10pm EDT

Independence Ballroom

William Sentosa, University of Illinois Urbana-Champaign; Balakrishnan Chandrasekaran, VU Amsterdam; P. Brighten Godfrey, University of Illinois Urbana-Champaign and Broadcom; Haitham Hassanieh, EPFL

The inherent variability of real-world cellular networks makes it hard to evaluate, reproduce, and debug the performance of networked applications running on these networks. A common approach is to record and replay a trace of observed cellular network performance. However, we show that the state-of-the-art record-and-replay technique produces empirically inaccurate results that can cause evaluation bias. This paper presents the design and implementation of CellReplay, a tool that records the time-varying performance of a live cellular network into traces using preset workloads and faithfully replays the observed performance for other workloads through an emulated network interface. The key challenge in achieving high accuracy is to replay varying network behavior in a way that captures its sensitivity to the workload. CellReplay records network behavior under two predefined workloads simultaneously and interpolates upon replay for other workloads. Across various challenging network conditions, our evaluation shows that real-world networked applications (e.g., web browsing or video streaming) running on CellReplay achieve similar performance (e.g., page load time or bitrate selection) to their live network counterparts, with significantly reduced error compared to the prior method.

https://www.usenix.org/conference/nsdi25/presentation/sentosa

Tuesday April 29, 2025 3:50pm - 4:10pm EDT
Independence Ballroom

Track 2

4:10pm EDT

Building an Elastic Block Storage over EBOFs Using Shadow Views

Tuesday April 29, 2025 4:10pm - 4:30pm EDT

Liberty Ballroom

Sheng Jiang, Carnegie Mellon University; Ming Liu, University of Wisconsin-Madison

The EBOF (Ethernet-Bunch-Of-Flash) has emerged as an enticing and promising disaggregated storage platform due to its streamlined I/O processing, high scalability, and substantial energy/cost-efficiency improvement. An EBOF applies a smart-sender dumb-receiver design philosophy and provides backward-compatible storage volumes to expedite system deployment. Yet, the static and opaque internal I/O processing pipeline lacks resource allocation, I/O scheduling, and traffic orchestration capabilities, entailing bandwidth waste, workload non-adaptiveness, and performance interference.

This paper presents the design and implementation of a distributed telemetry system (called shadow view) to tackle the above challenges and facilitate the effective use of an EBOF. We model an EBOF as a two-layer multi-switch architecture and develop a view development protocol to construct the EBOF running snapshot and expose internal execution statistics at runtime. Our design is motivated by the observation that fast data center networks make the overheads of inter-server communication and synchronization negligible. We demonstrate the effectiveness of shadow view by building a block storage (dubbed Flint) atop EBOFs. The enhanced I/O data plane allows us to develop new three techniques–an elastic volume manager, a view-enabled bandwidth auction mechanism, and an eIO scheduler. Our evaluations using the Fungible FS1600 EBOF show that a Flint volume achieves 9.3/9.2 GB/s read/write bandwidth with no latency degradation, significantly outperforming the defacto EBOF volume. It achieves up to 2.9× throughput improvements when running an object store. Flint is tenant-aware and remote target-aware, delivering efficient multi-tenancy and workload adaptiveness.

https://www.usenix.org/conference/nsdi25/presentation/jiang

Tuesday April 29, 2025 4:10pm - 4:30pm EDT
Liberty Ballroom

Track 1

4:10pm EDT

Large Network UWB Localization: Algorithms and Implementation

Tuesday April 29, 2025 4:10pm - 4:30pm EDT

Independence Ballroom

Nakul Garg and Irtaza Shahid, University of Maryland, College Park; Ramanujan K Sheshadri, Nokia Bell Labs; Karthikeyan Sundaresan, Georgia Institute of Technology; Nirupam Roy, University of Maryland, College Park

Localization of networked nodes is an essential problem in emerging applications, including first-responder navigation, automated manufacturing lines, vehicular and drone navigation, asset tracking, Internet of Things, and 5G communication networks. In this paper, we present Locate3D, a novel system for peer-to-peer node localization and orientation estimation in large networks. Unlike traditional range-only methods, Locate3D introduces angle-of-arrival (AoA) data as an added network topology constraint. The system solves three key challenges: it uses angles to reduce the number of measurements required by 4× and jointly uses range and angle data for location estimation. We develop a spanning-tree approach for fast location updates, and to ensure the output graphs are rigid and uniquely realizable, even in occluded or weakly connected areas. Locate3D cuts down latency by up to 75% without compromising accuracy, surpassing standard range-only solutions. It has a 0.86 meter median localization error for building-scale multi-floor networks (32 nodes, 0 anchors) and 12.09 meters for large-scale networks (100,000 nodes, 15 anchors).

https://www.usenix.org/conference/nsdi25/presentation/garg

Tuesday April 29, 2025 4:10pm - 4:30pm EDT
Independence Ballroom

Track 2

4:30pm EDT

Pushing the Limits of In-Network Caching for Key-Value Stores

Tuesday April 29, 2025 4:30pm - 4:50pm EDT

Liberty Ballroom

Gyuyeong Kim, Sungshin Women's University

We present OrbitCache, a new in-network caching architecture that can cache variable-length items to balance a wide range of key-value workloads. Unlike existing works, OrbitCache does not cache hot items in the switch memory. Instead, we make hot items revisit the switch data plane continuously by exploiting packet recirculation. Our approach keeps cached key-value pairs in the switch data plane while freeing them from item size limitations caused by hardware constraints. We implement an OrbitCache prototype on an Intel Tofino switch. Our experimental results show that OrbitCache can balance highly skewed workloads and is robust to various system conditions.

https://www.usenix.org/conference/nsdi25/presentation/kim

Tuesday April 29, 2025 4:30pm - 4:50pm EDT
Liberty Ballroom

Track 1

4:30pm EDT

Towards Energy Efficient 5G vRAN Servers

Tuesday April 29, 2025 4:30pm - 4:50pm EDT

Independence Ballroom

Anuj Kalia, Microsoft; Nikita Lazarev, MIT; Leyang Xue, The University of Edinburgh; Xenofon Foukas and Bozidar Radunovic, Microsoft; Francis Y. Yan, Microsoft Research and UIUC

We study the problem of improving energy efficiency in virtualized radio access network (vRAN) servers, focusing on CPUs. Two distinct characteristics of vRAN software—strict real-time sub-millisecond deadlines and its proprietary black-box nature—preclude the use of existing general-purpose CPU energy management techniques. This paper presents RENC, a system that saves energy by adjusting CPU frequency in response to sub-second variations in cellular workloads, using the following techniques. First, despite large fluctuations in vRAN CPU load at sub-ms timescales, RENC establishes safe low-load intervals, e.g., by coupling Media Access Control (MAC) layer rate limiting with CPU frequency changes. This prevents high traffic during low-power operation, which would otherwise cause deadline misses. Second, we design techniques to compute CPU frequencies that are safe for these low-load intervals, achieved by measuring the slack in vRAN threads' deadlines using Linux eBPF hooks, or minor binary rewriting of the vRAN software. Third, we demonstrate the need to handle CPU load spikes triggered by control operations, such as new users attaching to the network. Our evaluation in a state-of-the-art vRAN testbed shows that our techniques reduces a vRAN server's CPU power consumption by up to 45% (29% server-wide).

https://www.usenix.org/conference/nsdi25/presentation/kalia

Tuesday April 29, 2025 4:30pm - 4:50pm EDT
Independence Ballroom

Track 2

4:50pm EDT

Building Massive MIMO Baseband Processing on a Single-Node Supercomputer

Tuesday April 29, 2025 4:50pm - 5:10pm EDT

Independence Ballroom

Xincheng Xie, Wentao Hou, Zerui Guo, and Ming Liu, University of Wisconsin-Madison

The rising deployment of massive MIMO coupled with the wide adoption of virtualized radio access networks (vRAN) poses an unprecedented computational demand on the baseband processing, hardly met by existing vRAN hardware substrates. The single-node supercomputer, an emerging computing platform, offers scalable computation and communication capabilities, making it a promising target to hold and run the baseband pipeline. However, realizing this is non-trivial due to the mismatch between (a) the diverse execution granularities and incongruent parallel degrees of different stages along the software processing pipeline and (b) the underlying evolving irregular hardware parallelism at runtime.

This paper closes the gap by designing and implementing MegaStation–an application-platform co-designed system that effectively harnesses the computing power of a single-node supercomputer for processing massive MIMO baseband. Our key insight is that one can adjust the execution granularity and reconstruct the baseband processing pipeline on the fly based on the monitored hardware parallelism status. Inspired by dynamic instruction scheduling, MegaStation models the single-node supercomputer as a tightly coupled microprocessor and employs a scoreboarding-like algorithm to orchestrate "baseband processing" instructions over GPU-instantiated executors. Our evaluations using the GigaIO FabreX demonstrate that MegaStation achieves up to 66.2% lower tail frame processing latency and 4× higher throughput than state-of-the-art solutions. MegaStation is a scalable and adaptive solution that can meet today’s vRAN requirements.

https://www.usenix.org/conference/nsdi25/presentation/xie

Tuesday April 29, 2025 4:50pm - 5:10pm EDT
Independence Ballroom

Track 2

5:10pm EDT

Efficient Multi-WAN Transport for 5G with OTTER

Tuesday April 29, 2025 5:10pm - 5:30pm EDT

Independence Ballroom

Mary Hogan, Oberlin College; Gerry Wan, Google; Yiming Qiu, University of Michigan; Sharad Agarwal and Ryan Beckett, Microsoft; Rachee Singh, Cornell University; Paramvir Bahl, Microsoft

In the ongoing cloudiﬁcation of 5G, software network functions (NFs) are replacing ﬁxed-function network hardware, allowing 5G network operators to leverage the beneﬁts of cloud computing. The migration of NFs and their management to the cloud causes 5G trafﬁc to traverse an operator’s wide-area network (WAN) to the cloud WAN that hosts the datacenters (DCs) running 5G NFs and applications. However, achieving end-to-end (E2E) performance for 5G trafﬁc across two WANs is hard. Placing 5G ﬂows across two WANs with different performance and reliability characteristics, edge and DC resource constraints, and interference from other ﬂows is different and more challenging than single-WAN trafﬁc engineering. We address this challenge and show that orchestrating E2E paths across a multi-WAN overlay allows us to achieve average 13% more throughput, 15% less RTT, 45% less jitter, or reduce average loss from 0.06% to under 0.001%. We implement our multi-WAN 5G ﬂow placement in a scalable optimization prototype that allocates 26%–45% more bytes on the network than greedy baselines while also satisfying the service demands of more ﬂows.

https://www.usenix.org/conference/nsdi25/presentation/hogan

Tuesday April 29, 2025 5:10pm - 5:30pm EDT
Independence Ballroom

Track 2

6:00pm EDT

NSDI '25 Poster Session and Reception

Tuesday April 29, 2025 6:00pm - 7:30pm EDT

Franklin Hall A

Check out the cool new ideas and the latest preliminary research on display at the Poster Session and Reception. Enjoy dinner, drinks, and the chance to connect with other attendees, authors, and symposium organizers.

Tuesday April 29, 2025 6:00pm - 7:30pm EDT
Franklin Hall A

Activities

7:30pm EDT

Meta Sponsor Event

Tuesday April 29, 2025 7:30pm - 8:30pm EDT

Franklin Hall 5-6

Join Meta to learn more about current projects and enjoy refreshments while networking with other attendees.

Sponsors

Meta

Tuesday April 29, 2025 7:30pm - 8:30pm EDT
Franklin Hall 5-6

Activities

8:00am EDT

Continental Breakfast

Wednesday April 30, 2025 8:00am - 9:00am EDT

Liberty Ballroom Foyer

Wednesday April 30, 2025 8:00am - 9:00am EDT
Liberty Ballroom Foyer

8:00am EDT

Badge Pickup

Wednesday April 30, 2025 8:00am - 12:00pm EDT

Liberty Ballroom Foyer

Wednesday April 30, 2025 8:00am - 12:00pm EDT
Liberty Ballroom Foyer

9:00am EDT

Verifying maximum link loads in a changing world

Wednesday April 30, 2025 9:00am - 9:20am EDT

Liberty Ballroom

Tibor Schneider, ETH Zürich; Stefano Vissicchio, University College London; Laurent Vanbever, ETH Zürich

To meet ever more stringent requirements, network operators often need to reason about worst-case link loads. Doing so involves analyzing traffic forwarding after failures and BGP route changes. State-of-the-art systems identify failure scenarios causing congestion, but they ignore route changes.

We present Velo, the first verification system that efficiently finds maximum link loads under failures and route changes. The key building block of Velo is its ability to massively reduce the gigantic space of possible route changes thanks to (i) a router-based abstraction for route changes, (ii) a theoretical characterization of scenarios leading to worst-case link loads, and (iii) an approximation of input traffic matrices. We fully implement and extensively evaluate Velo. Velo takes only a few minutes to accurately compute all worst-case link loads in large ISP networks. It thus provides operators with critical support to robustify network configurations, improve network management and take business decisions.

https://www.usenix.org/conference/nsdi25/presentation/schneider

Wednesday April 30, 2025 9:00am - 9:20am EDT
Liberty Ballroom

Track 1

9:20am EDT

A Layered Formal Methods Approach to Answering Queue-related Queries

Wednesday April 30, 2025 9:20am - 9:40am EDT

Liberty Ballroom

Divya Raghunathan, Maria Apostolaki, and Aarti Gupta, Princeton University

Queue dynamics introduce significant uncertainty in network management tasks such as debugging, performance monitoring, and analysis. Despite numerous queue-monitoring techniques, many networks today continue to collect only per-port packet counts (e.g., using SNMP). Although queue lengths are correlated with packet counts, deriving the precise correlation between them is very challenging since packet counts do not specify many quantities (e.g., packet arrival order) which affect queue lengths.

This paper presents QuASI, a system that can answer many queue-related queries using only coarse-grained per-port packet counts. QuASI checks whether there exists a packet trace that is consistent with the packet counts and satisfies a query. To scale on large problem instances, QuASI relies on a layered approach and on a novel enqueue-rate abstraction, which is lossless for the class of queries that QuASI answers. The first layer employs a novel and efficient algorithm that generates a cover-set of abstract traces, constructs representative abstract traces from the cover-set, and efficiently checks each representative abstract trace by leveraging a known result on (0,1)-matrix existence. The first layer guarantees no false negatives: if the first layer says "No", there is no packet trace consistent with the observed packet counts that makes the query true. If it says "Yes", further verification is needed, which the second layer resolves using an SMT solver. As a result, QuASI has no false positives and no false negatives.

Our evaluations show that QuASI is up to 10⁶X faster than state-of-the-art, and can answer non-trivial queries about queue metrics (e.g., queue length) using minute-granularity packet counts. Our work is the first step toward more practical formal performance analysis under given measurements.

https://www.usenix.org/conference/nsdi25/presentation/raghunathan

Wednesday April 30, 2025 9:20am - 9:40am EDT
Liberty Ballroom

Track 1

9:40am EDT

Runtime Protocol Refinement Checking for Distributed Protocol Implementations

Wednesday April 30, 2025 9:40am - 10:00am EDT

Liberty Ballroom

Ding Ding, Zhanghan Wang, Jinyang Li, and Aurojit Panda, NYU

Despite significant progress in verifying protocols, services that implement distributed protocols (we refer to these as DPIs in what follows), e.g., Chubby or Etcd, can exhibit safety bugs in production deployments. These bugs are often introduced by programmers when converting protocol descriptions into code. This paper introduces Runtime Protocol Refinement Checking (RPRC) a runtime approach for detecting protocol implementation bugs in DPIs. RPRC systems observe a deployed DPI's runtime behavior and notify operators when this behavior evidences a protocol implementation bug, allowing operators to mitigate the bugs impact and developers to fix the bug. We have developed an algorithm for RPRC and implemented it in a system called Ellsberg that targets DPIs that assume fail-stop failures and the asynchronous (or partially synchronous) model. Our goal when designing Ellsberg was to make no assumptions about how DPIs are implemented, and to avoid additional coordination or communication. Therefore, Ellsberg builds on the observation that in the absence of Byzantine failures, a protocol safety properties are maintained if all live DPI processes correctly implement the protocol. Thus, Ellsberg checks RPRC by comparing messages sent and received by each DPI process to those produced by a simulated execution of the protocol. We apply Ellsberg to three open source DPIs, Etcd, Zookeeper and Redis Raft, and show that we can detect previously reported protocol bugs in these DPIs.

https://www.usenix.org/conference/nsdi25/presentation/ding

Wednesday April 30, 2025 9:40am - 10:00am EDT
Liberty Ballroom

Track 1

10:00am EDT

CEGS: Configuration Example Generalizing Synthesizer

Wednesday April 30, 2025 10:00am - 10:20am EDT

Liberty Ballroom

Jianmin Liu, Tsinghua University; Li Chen, Zhongguancun Laboratory; Dan Li, Tsinghua University; Yukai Miao, Zhongguancun Laboratory

Network configuration synthesis promises to increase the efficiency of network management by reducing human involvement. However, despite significant advances in this field, existing synthesizers still require much human effort in drafting configuration templates or coding in a domain-specific language. We argue that the main reason for this is that a core capability is missing for current synthesizers: identifying and following configuration examples in configuration manuals and generalizing them to arbitrary topologies.

In this work, we fill this capability gap with two recent advancements in artificial intelligence: graph neural networks (GNNs) and large language models (LLMs). We build CEGS, which can automatically identify appropriate configuration examples, follow and generalize them to fit target network scenarios. CEGS features a GNN-based Querier to identify relevant examples from device documentations, a GNN-based Classifier to generalize the example to arbitrary topology, and an efficient LLM-driven synthesis method to quickly and correctly synthesize configurations that comply with the intents. Evaluations of real-world networks and complex intents show that CEGS can automatically synthesize correct configurations for a network of 1094 devices without human involvement. In contrast, the state-of-the-art LLM-based synthesizer are more than 30 times slower than CEGS on average, even when human experts are in the loop.

https://www.usenix.org/conference/nsdi25/presentation/liu-jianmin

Wednesday April 30, 2025 10:00am - 10:20am EDT
Liberty Ballroom

Track 1

10:20am EDT

Coffee and Tea Break

Wednesday April 30, 2025 10:20am - 10:50am EDT

Liberty Ballroom Foyer

Wednesday April 30, 2025 10:20am - 10:50am EDT
Liberty Ballroom Foyer

10:50am EDT

Suppressing BGP Zombies with Route Status Transparency

Wednesday April 30, 2025 10:50am - 11:10am EDT

Liberty Ballroom

Yosef Edery Anahory, The Hebrew University of Jerusalem; Jie Kong, Nicholas Scaglione, and Justin Furuness, University of Connecticut; Hemi Leibowitz, The College of Management Academic Studies; Amir Herzberg and Bing Wang, University of Connecticut; Yossi Gilad, The Hebrew University of Jerusalem

Withdrawal suppression has been a known weakness of BGP for over a decade. It has a significant detrimental impact on both the reliability and security of inter-domain routing on the Internet. This paper presents Route Status Transparency (RoST), the first design that efficiently and securely thwarts withdrawal suppression misconfigurations and attacks. RoST allows ASes to efficiently verify whether a route has been withdrawn; it is compatible with BGP as well as with BGP security enhancements. We use simulations on the Internet’s AS-level topology to evaluate the benefits from adopting RoST. We use an extensive real-world BGP announcements dataset to show that it is efficient in terms of storage, bandwidth, and computational requirements.

https://www.usenix.org/conference/nsdi25/presentation/anahory

Wednesday April 30, 2025 10:50am - 11:10am EDT
Liberty Ballroom

Track 1

11:10am EDT

ValidaTor: Domain Validation over Tor

Wednesday April 30, 2025 11:10am - 11:30am EDT

Liberty Ballroom

Jens Frieß, National Research Center for Applied Cybersecurity ATHENE and Technische Universität Darmstadt; Haya Schulmann, National Research Center for Applied Cybersecurity ATHENE and Goethe-Universität Frankfurt; Michael Waidner, National Research Center for Applied Cybersecurity ATHENE and Technische Universität Darmstadt

Domain Validation (DV) is the primary method used by Certificate Authorities (CAs) to confirm administrative control over a domain before issuing digital certificates. Despite its widespread use, DV is vulnerable to various attacks, prompting the adoption of multiple vantage points to enhance security, such as the state of the art DV mechanism supported by Let’s Encrypt. However, even distributed static vantage points remain susceptible to targeted attacks. In this paper we introduce ValidaTor, an HTTP-based domain validation system that leverages the Tor network to create a distributed and unpredictable set of validators. By utilizing Tor’s exit nodes, ValidaTor significantly increases the pool of available validators, providing high path diversity and resilience against strong adversaries. Our empirical evaluations demonstrate that ValidaTor can achieve the validation throughput of a commercial CA and
has the potential to scale to a validation volume comparable to Let’s Encrypt, while using minimal dedicated infrastructure and only a small fraction (~0.1%) of Tor’s available bandwidth. While unpredictable selection of validators makes ValidaTor fully resistant to targeted attacks on validators, we
also show the use of Tor nodes improves path diversity and thereby the resilience of DV to subversion by well-positioned ASes, reducing the number of Autonomous Systems (ASes) capable of issuing fraudulent certificates by up to 27% compared to Let’s Encrypt. Lastly, we show that the chance of subversion by malicious, colluding exit nodes is negligible (≤ 1% even with a quarter of existing exit nodes). We make the code of ValidaTor as well as the datasets and measurements publicly available for use, reproduction, and future research.

https://www.usenix.org/conference/nsdi25/presentation/friess

Wednesday April 30, 2025 11:10am - 11:30am EDT
Liberty Ballroom

Track 1

11:30am EDT

From Address Blocks to Authorized Prefixes: Redesigning RPKI ROV with a Hierarchical Hashing Scheme for Fast and Memory-Efficient Validation

Wednesday April 30, 2025 11:30am - 11:50am EDT

Liberty Ballroom

Zedong Ni, Computer Network Information Center, Chinese Academy of Sciences; and School of Cyber Science & Engineering, Southeast University; Yinbo Xu, Hui Zou, and Yanbiao Li, Computer Network Information Center, Chinese Academy of Sciences; and University of Chinese Academy of Sciences; Guang Cheng, School of Cyber Science & Engineering, Southeast University; and Purple Mountain Laboratories; Gaogang Xie, Computer Network Information Center, Chinese Academy of Sciences; and University of Chinese Academy of Sciences

Route Origin Validation (ROV) with Route Origin Authorizations (ROAs), built on top of the Resource Public Key Infrastructure (RPKI), serves as the only formally standardized and production-grade defense mechanism against route hijackings in global interdomain routing infrastructures. However, the widespread adoption of RPKI has introduced escalating scalability challenges in validating high volumes of route messages against massive ROA entries.

In this paper, we attribute the performance bottleneck of existing ROV schemes to their underlying validation model, where the route is matched against rules in the form of address blocks. To overcome this bottleneck, we propose the Authorized Prefix (AP) model that enables validation at the prefix granularity, and redesign RPKI ROV based on this new model with a hierarchical hashing scheme named h²ROV. Extensive evaluations verify h²ROV's superiority over state-of-the-art approaches in IPv4, with a speedup of $1.7× ∼ 9.8× in validation and a reduction of 49.3% ∼ 86.6% in memory consumption. System emulations using real-world network topologies further demonstrate h²ROV confines its impact to routing convergence to below 8.5% during update burst events, while reducing ROV-induced delays by 30.4% ∼ 64.7% compared to existing solutions.

https://www.usenix.org/conference/nsdi25/presentation/ni

Wednesday April 30, 2025 11:30am - 11:50am EDT
Liberty Ballroom

Track 1

11:50am EDT

PreAcher: Secure and Practical Password Pre-Authentication by Content Delivery Networks

Wednesday April 30, 2025 11:50am - 12:10pm EDT

Liberty Ballroom

Shihan Lin, Duke University; Suting Chen, Northwestern University; Yunming Xiao, University of Michigan; Yanqi Gu, University of California, Irvine; Aleksandar Kuzmanovic, Northwestern University; Xiaowei Yang, Duke University

In today's Internet, websites widely rely on password authentication for user logins. However, the intensive computation required for password authentication exposes web servers to Application-layer DoS (ADoS) attacks that exploit the login interfaces. Existing solutions fail to simultaneously prevent such ADoS attacks, preserve password secrecy, and maintain good usability. In this paper, we present PreAcher, a system architecture that incorporates third-party Content Delivery Networks (CDNs) into the password authentication process and offloads the authentication workload to CDNs without divulging the passwords to them. At the core of PreAcher is a novel three-party authentication protocol that combines Oblivious Pseudorandom Function (OPRF) and Locality-Sensitive Hashing (LSH). This protocol allows CDNs to pre-authenticate users and thus filter out ADoS traffic without compromising password security. Our evaluations demonstrate that PreAcher significantly enhances the resilience of web servers against both ADoS attacks and preserves password security while introducing acceptable overheads. Notably, PreAcher can be deployed immediately by websites alone today, without modifications to client software or CDN infrastructure. We release the source code of PreAcher to facilitate its deployment and future research.

https://www.usenix.org/conference/nsdi25/presentation/lin-shihan

Wednesday April 30, 2025 11:50am - 12:10pm EDT
Liberty Ballroom

Track 1

12:10pm EDT

Lunch (on your own)

Wednesday April 30, 2025 12:10pm - 2:00pm EDT

Wednesday April 30, 2025 12:10pm - 2:00pm EDT
-

2:00pm EDT

ClubHeap: A High-Speed and Scalable Priority Queue for Programmable Packet Scheduling

Wednesday April 30, 2025 2:00pm - 2:20pm EDT

Liberty Ballroom

Zhikang Chen, Tsinghua University; Haoyu Song, Futurewei Technologies; Zhiyu Zhang and Yang Xu, Fudan University; Bin Liu, Tsinghua University

While PIFO is a powerful priority queue abstraction to support programmable packet scheduling in network devices, the efficient implementation of PIFO faces multiple challenges in performance and scalability. The existing solutions all fall short of certain requirements. In this paper, we propose ClubHeap to address the problem. On the one hand, we develop a novel hardware-friendly heap data structure to support faster PIFO queue operations that can schedule a flow in every clock cycle, reaching the theoretical lower bound; on the other hand, the optimized hardware architecture reduces the circuit complexity and thus enables a higher clock frequency. The end result is the best scheduling performance in its class. Combined with its inherently better scalability and flexibility, ClubHeap is an ideal solution to be built in programmable switches and SmartNICs to support various scheduling algorithms. We build an FPGA-based hardware prototype and conduct a thorough evaluation by comparing ClubHeap with the other state-of-the-art solutions. ClubHeap also allows graceful trade-offs between throughput and resource consumption through parameter adjustments, making it adaptable on different target devices.

https://www.usenix.org/conference/nsdi25/presentation/chen-zhikang

Wednesday April 30, 2025 2:00pm - 2:20pm EDT
Liberty Ballroom

Track 1

2:20pm EDT

Self-Clocked Round-Robin Packet Scheduling

Wednesday April 30, 2025 2:20pm - 2:40pm EDT

Liberty Ballroom

Erfan Sharafzadeh, Johns Hopkins University and Hewlett Packard Labs; Raymond Matson, University of California Riverside; Jean Tourrilhes and Puneet Sharma, Hewlett Packard Labs; Soudeh Ghorbani, Johns Hopkins University and Meta

Deficit Round Robin (DRR) is the de facto fair packet scheduler in the Internet due to its superior fairness and scalability. We show that DRR can perform poorly due to its assumptions about packet size distributions and traffic bursts. Concretely, DRR performs best if (1) packet size distributions are known in advance; its optimal performance depends on tuning a parameter based on the largest packet, and (2) all bursts are long and create backlogged queues. We show that neither of these assumptions holds in today's Internet: packet size distributions are varied and dynamic, complicating the tuning of DRR. Plus, Internet traffic consists of many short, latency-sensitive flows, creating small bursts. These flows can experience high latency under DRR as it serves a potentially large number of flows in a round-robin fashion.

To address these shortcomings while retaining the fairness and scalability of DRR, we introduce Self-Clocked Round-Robin Scheduling (SCRR), a parameter-less, low-latency, and scalable packet scheduler that boosts short latency-sensitive flows through careful adjustments to their virtual times without violating their fair share guarantees. We evaluate SCRR using theoretical models and a Linux implementation on a physical testbed. Our results demonstrate that while performing on an equal footing with DRR on achieving flow fairness, SCRR reduces the average CPU overhead by 23% compared to DRR with a small quantum while improving the application latency by 71% compared to DRR with a large quantum.

https://www.usenix.org/conference/nsdi25/presentation/sharafzadeh

Wednesday April 30, 2025 2:20pm - 2:40pm EDT
Liberty Ballroom

Track 1

2:40pm EDT

Everything Matters in Programmable Packet Scheduling

Wednesday April 30, 2025 2:40pm - 3:00pm EDT

Liberty Ballroom

Albert Gran Alcoz, ETH Zürich; Balázs Vass, BME-TMIT; Pooria Namyar, USC; Behnaz Arzani, Microsoft Research; Gábor Rétvári, BME-TMIT; Laurent Vanbever, ETH Zürich

Operators can deploy any scheduler they desire on existing switches through programmable packet schedulers: they tag packets with ranks (which indicate their priority) and schedule them in the order of these ranks. The ideal programmable scheduler is the Push-In First-Out (PIFO) queue, which schedules packets in a perfectly sorted order by "pushing" packets into any position of the queue based on their ranks. However, it is hard to implement PIFO queues in hardware due to their need to sort packets at line rate (based on their ranks).

Recent proposals approximate PIFO behaviors on existing data-planes. While promising, they fail to simultaneously capture both of the necessary behaviors of PIFO queues: their scheduling behavior and admission control. We introduce PACKS, an approximate PIFO scheduler that addresses this problem. PACKS runs on top of a set of priority queues and uses packet-rank information and queue-occupancy levels during enqueue to determine whether to admit each incoming packet and to which queue it should be mapped.

We fully implement PACKS in P4 and evaluate it on real workloads. We show that PACKS better-approximates PIFO than state-of-the-art approaches. Specifically, PACKS reduces the rank inversions by up to 7× and 15× with respect to SP-PIFO and AIFO, and the number of packet drops by up to 60% compared to SP-PIFO. Under pFabric ranks, PACKS reduces the mean FCT across small flows by up to 33% and 2.6×, compared to SP-PIFO and AIFO. We also show that PACKS runs at line rate on existing hardware (Intel Tofino).

https://www.usenix.org/conference/nsdi25/presentation/alcoz

Wednesday April 30, 2025 2:40pm - 3:00pm EDT
Liberty Ballroom

Track 1

3:00pm EDT

When P4 Meets Run-to-completion Architecture

Wednesday April 30, 2025 3:00pm - 3:20pm EDT

Liberty Ballroom

Hao Zheng, State Key Laboratory for Novel Software Technology, Nanjing University, China; Xin Yan, Huawei, China; Wenbo Li, Jiaqi Zheng, and Xiaoliang Wang, State Key Laboratory for Novel Software Technology, Nanjing University, China; Qingqing Zhao, Luyou He, Xiaofei Lai, Feng Gao, and Fuguang Huang, Huawei, China; Wanchun Dou, Guihai Chen, and Chen Tian, State Key Laboratory for Novel Software Technology, Nanjing University, China

P4 programmable data planes have significantly accelerated the evolution of various network technologies. Although the P4 language has gained wide acceptance, its further development encounters two obstacles: limited programmability and the cessation of the next-generation Tofino chip. As a hardware manufacturer, we try to address the above dilemmas by opening the P4 programmability of our run-to-completion (RTC) chips. At present, there is no publicly available experience in this field. We introduce P4RTC, a comprehensive consolidation of our experiences applying the P4 language to RTC architecture. P4RTC introduces a new P4 architecture model and a set of beneficial extern constructs to fully leverage the RTC architecture’s programmability. Besides, we share the insights we have gained from designing and implementing compilers. We also provide a performance model to facilitate profiling P4RTC’s performance on user-customized P4 code. We prototype P4RTC on an RTC chip with 1.2 Tbps bandwidth. Case-oriented evaluation demonstrates that P4RTC can enhance P4 programmability and reduce the burdens of RTC development. The performance model can provide substantial insights into optimizing P4RTC programs.

https://www.usenix.org/conference/nsdi25/presentation/zheng-hao

Wednesday April 30, 2025 3:00pm - 3:20pm EDT
Liberty Ballroom

Track 1

3:20pm EDT

Coffee and Tea Break

Wednesday April 30, 2025 3:20pm - 3:50pm EDT

Liberty Ballroom Foyer

Wednesday April 30, 2025 3:20pm - 3:50pm EDT
Liberty Ballroom Foyer

3:50pm EDT

Mutant: Learning Congestion Control from Existing Protocols via Online Reinforcement Learning

Wednesday April 30, 2025 3:50pm - 4:10pm EDT

Liberty Ballroom

Lorenzo Pappone, Computer Science Department, Saint Louis University; Alessio Sacco, DAUIN, Politecnico di Torino; Flavio Esposito, Computer Science Department, Saint Louis University

Learning how to control congestion remains a challenge despite years of progress. Existing congestion control protocols have demonstrated efficacy within specific network conditions, inevitably behaving suboptimally or poorly in others. Machine learning solutions to congestion control have been proposed, though relying on extensive training and specific network configurations. In this paper, we loosen such dependencies by proposing Mutant, an online reinforcement learning algorithm for congestion control that adapts to the behavior of the best-performing schemes, outperforming them in most network conditions. Design challenges included determining the best protocols to learn from, given a network scenario, and creating a system able to evolve to accommodate future protocols with minimal changes. Our evaluation on real-world and emulated scenarios shows that Mutant achieves lower delays and higher throughput than prior learning-based schemes while maintaining fairness by exhibiting negligible harm to competing flows, making it robust across diverse and dynamic network conditions.

https://www.usenix.org/conference/nsdi25/presentation/pappone

Wednesday April 30, 2025 3:50pm - 4:10pm EDT
Liberty Ballroom

Track 1

4:10pm EDT

CATO: End-to-End Optimization of ML-Based Traffic Analysis Pipelines

Wednesday April 30, 2025 4:10pm - 4:30pm EDT

Liberty Ballroom

Gerry Wan, Stanford University; Shinan Liu, University of Chicago; Francesco Bronzino, ENS Lyon; Nick Feamster, University of Chicago; Zakir Durumeric, Stanford University

Machine learning has shown tremendous potential for improving the capabilities of network traffic analysis applications, often outperforming simpler rule-based heuristics. However, ML-based solutions remain difficult to deploy in practice. Many existing approaches only optimize the predictive performance of their models, overlooking the practical challenges of running them against network traffic in real time. This is especially problematic in the domain of traffic analysis, where the efficiency of the serving pipeline is a critical factor in determining the usability of a model. In this work, we introduce CATO, a framework that addresses this problem by jointly optimizing the predictive performance and the associated systems costs of the serving pipeline. CATO leverages recent advances in multi-objective Bayesian optimization to efficiently identify Pareto-optimal configurations, and automatically compiles end-to-end optimized serving pipelines that can be deployed in real networks. Our evaluations show that compared to popular feature optimization techniques, CATO can provide up to 3600× lower inference latency and 3.7× higher zero-loss throughput while simultaneously achieving better model performance.

https://www.usenix.org/conference/nsdi25/presentation/wan-gerry

Wednesday April 30, 2025 4:10pm - 4:30pm EDT
Liberty Ballroom

Track 1

4:30pm EDT

Resolving Packets from Counters: Enabling Multi-scale Network Traffic Super Resolution via Composable Large Traffic Model

Wednesday April 30, 2025 4:30pm - 4:50pm EDT

Liberty Ballroom

Xizheng Wang, Tsinghua University and Zhongguancun Laboratory; Libin Liu and Li Chen, Zhongguancun Laboratory; Dan Li, Tsinghua University; Yukai Miao and Yu Bai, Zhongguancun Laboratory

Realistic fine-grained traffic traces are valuable to numerous applications in both academia and industry. However, obtaining them directly from devices is significantly challenging, while coarse-grained counters are readily available on almost all network devices. None of existing work can restore fine-grained traffic traces from counters, which we call network traffic super-resolution (TSR). To this end, we propose ZOOMSYNTH, the first TSR system that can achieve packet-level trace synthesis with counter traces as input. Following the basic structure of the TSR task, we design the Granular Traffic Transformer (GTT) model and the Composable Large Traffic Model (CLTM). CLTM is a tree of GTT models, and the GTT models in each layer perform upscaling on a particular granularity, which allows each GTT model to capture the traffic characteristics at this resolution. Using CLTM, we synthesize fine-grained traces from counters. We also leverage a rule-following model to comprehend counter rules (e.g. ACLs) when available, guiding the generations of fine-grained traces. We implement ZOOMSYNTH and perform extensive evaluations. Results show that, with only second-level counter traces, ZOOMSYNTH achieves synthesis quality comparable to existing solutions that takes packet-level traces as input. CLTM can also be fine-tuned to support downstream tasks. For example, ZOOMSYNTH with fine-tuned CLTM outperforms the existing solution by 27.5% and 9.8% in anomaly detection and service recognition tasks, respectively. To promote future research, we release the pre-trained CLTM-1.8B model weights along with its source code.

https://www.usenix.org/conference/nsdi25/presentation/wang-xizheng-resolving

Wednesday April 30, 2025 4:30pm - 4:50pm EDT
Liberty Ballroom

Track 1

4:50pm EDT

BFTBrain: Adaptive BFT Consensus with Reinforcement Learning

Wednesday April 30, 2025 4:50pm - 5:10pm EDT

Liberty Ballroom

Chenyuan Wu and Haoyun Qin, University of Pennsylvania; Mohammad Javad Amiri, Stony Brook University; Boon Thau Loo, University of Pennsylvania; Dahlia Malkhi, UC Santa Barbara; Ryan Marcus, University of Pennsylvania

This paper presents BFTBrain, a reinforcement learning (RL) based Byzantine fault-tolerant (BFT) system that provides significant operational benefits: a plug-and-play system suitable for a broad set of hardware and network configurations, and adjusts effectively in real-time to changing fault scenarios and workloads. BFTBrain adapts to system conditions and application needs by switching between a set of BFT protocols in real-time. Two main advances contribute to BFTBrain’s agility and performance. First, BFTBrain is based on a systematic, thorough modeling of metrics that correlate the performance of the studied BFT protocols with varying fault scenarios and workloads. These metrics are fed as features to BFTBrain’s RL engine in order to choose the best-performing BFT protocols in real-time. Second, BFTBrain coordinates RL in a decentralized manner which is resilient to adversarial data pollution, where nodes share local metering values and reach the same learning output by consensus. As a result, in addition to providing significant operational benefits, BFTBrain improves throughput over fixed protocols by 18% to 119% under dynamic conditions and outperforms state-of-the-art learning based approaches by 44% to 154%.

https://www.usenix.org/conference/nsdi25/presentation/wu-chenyuan

Wednesday April 30, 2025 4:50pm - 5:10pm EDT
Liberty Ballroom

Track 1