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Computer Science Networking

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Monday 28 September

Networking

Configuration-Induced Delivery Failures in NATS JetStream: Detection and Remediation

Biplab Kumar Das

NATS JetStream's at-least-once delivery guarantee is conditional: five common configuration mistakes silently violate it, causing duplicate message processing, data loss, or redelivery storms with no error logged anywhere. The standard Prometheus NATS exporter exposes only server-level throughput metrics and cannot detect any of these failures. We present nats-lens, a standalone monitor that reads from the JetStream management API and detects all five violation classes without requiring changes to monitored applications or client code. We formally characterize each class with a precise condition, prove that standard Prometheus NATS metrics are structurally incapable of detecting any of them, and implement five targeted detectors. In a controlled evaluation of 30 rounds per scenario on both single-node and 3-node JetStream clusters, nats-lens achieves 100% detection coverage across all five classes—versus 0% for the baseline—with zero false positives over 30 minutes of healthy operation. Detection latency ranges from 2,003 ms to 8,013 ms (within three poll cycles). We confirm language-agnostic detection empirically using consumers in Rust, Go, and Python. The tool is open source and exposes findings through four output channels: web dashboard, Prometheus metrics, REST API, and NATS health events.
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Networking · Cross-listed from cs.DC

Weaver: A System for AI-RAN Compute Sharing with Foundation Model Training

Leyang Xue, Tianxin Wang, Xin Zhe Khooi et al. (+6 more)

The emergence of AI-RAN infrastructure, which equips cell sites with GPU-accelerated hardware, creates an opportunity to colocate non-RAN workloads with primary RAN processing. We explore using this spare capacity for decentralized training of foundation models (FMs), one of the most compute-intensive AI workloads. We present the first characterization of spare GPU capacity in AI-RAN systems at both micro-scale–across transmission slots within a cell site–and macro-scale–across sites. Our analysis finds that 40-85% of GPU capacity is unused; although this capacity is temporally bursty at individual sites, it is spatially complementary across sites. To safely and efficiently harness these resources, we present Weaver, a system that opportunistically trains FMs alongside latency-critical RAN workloads without degrading RAN performance. Weaver adopts a RAN-first design: a spare-compute controller integrated into the MAC scheduler uses compute-aware scheduling to smooth RAN GPU demand and exposes more usable spare GPU capacity. A two-level elastic training framework then adapts to dynamic, heterogeneous spare capacity within and across sites. Experiments on an O-RAN-aligned system prototype show that Weaver creates up to 4.9x more usable spare compute and utilizes up to 83% of the available spare capacity. On a multi-site testbed, Weaver improves training throughput by 2.1-3.7x over baseline approaches.
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All authors Leyang Xue, Tianxin Wang, Xin Zhe Khooi, Jiaxun Yang, Dheeraj Mahendiran, Yufeng Xia, Mun Choon Chan, Myungjin Lee, Mahesh K. Marina

Networking · Cross-listed from cs.CR

Implementing Data Diodes Using Commodity Hardware and Open Source Software

Peter Story and Gert-Jan den Besten

One-way network devices, known as data diodes, are used to defend against sophisticated cyberattacks. Partly due to their high cost, data diodes are mostly deployed in nuclear power plants and within the government for handling classified information. Although commercially available data diodes are expensive, a data diode's hardware can assembled from commodity fiber-optic network equipment. However, specialized software is needed to send data through a data diode reliably: the receiving program cannot request retransmission of dropped packets, so packet loss must be minimized and mitigated. First, we developed a minimal program to measure packet loss. We found that most packet loss was caused by the receiving program processing incoming packets too slowly, and that packet loss often occurs in clusters. Also, we discovered ways to minimize packet loss on Linux and macOS without using superuser privileges. Next, we tested three existing open source programs for one-way data transfers: netcat, UDPcast, and lidi. Although these programs were unreliable in their default configurations, we identified reliable configurations for UDPcast and lidi. Finally, we incorporated our findings into pydiode, our cross-platform program for reliable one-way data transfers.
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Networking

Edge-Assisted Multi-View Localization for Low-Altitude Economy under GPS-Challenged Environments

Zhengru Fang, Huanhuan Lou, Senkang Hu et al. (+5 more)

Unmanned aerial vehicles (UAVs) serving the low-altitude economy require reliable localization in urban canyons, indoor facilities, and other GPS-challenged environments. Visual matching with a geo-tagged database provides an alternative source for absolute positioning, but onboard computation and energy limits motivate offloading the database and matching pipeline to an edge server. The resulting localization quality depends on what visual information can reach the edge in time under varying wireless-communication and edge-computing resources. In this paper, we propose a network-adaptive edge-assisted multi-view localization framework that combines scalable orthogonality-regularized variational information bottleneck (O-VIB) encoding, value-of-information (VOI)-guided request control, and value-aware edge scheduling. We design an O-VIB model that supports nested latent prefixes from 8 to 128 dimensions and four UAV view modes. Each UAV requests edge assistance when the predicted localization-risk reduction exceeds the communication and service costs. On CARLA multi-view UAV data, VOI-guided control can lower the mean and 95th-percentile (p95) route errors by 24.8% and 31.0%, respectively, relative to budgeted periodic offloading under a matched per-route traffic budget. In indoor UAV experiments with motion-capture ground truth, our design can lower the mean position error by 28.0% relative to uncompressed all-view CLIP retrieval while cutting the descriptor traffic by 98.6%, using a 0.145 KB semantic representation. Under high congestion, a VOI-weighted scheduler with waiting-age and deadline shaping can lower the edge-side p95 latency of the top-10% high-value requests from 137.7 ms to 32.8 ms.
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All authors Zhengru Fang, Huanhuan Lou, Senkang Hu, Yihang Tao, Zongdian Li, Yiqin Deng, Jingjing Wang, Yuguang Fang

Networking

Deconstructing BLE Multi-hop: a Model-based Approach to Quantifying the Challenges

Bozheng Pang, José Alamos, Thomas C. Schmidt et al. (+1 more)

Bluetooth Low Energy (BLE) was originally designed for point-to-point communication, but BLE multi-hop networks have attracted academic and industrial interest. Multi-hop communication, however, introduces desynchronized nodes to the network due to the difference in individual clocks. In this paper, we study the impact of clock drift on a BLE multi-hop network. The aim is to find out if a BLE multi-hop network can be properly set up, why, and how. For this goal, we develop a mathematical model to quantify the collision probability among BLE connections managed by a single device. We provide a simplified version of the model for constrained deployment. The impact of clock drift and our mathematical model are validated through practical experiments. Afterward, we find that a BLE multi-hop network cannot eliminate the impact of clock drift in most cases. Moreover, from a long-term point of view, the collision between BLE connections occurs with a fixed frequency. We further analyze the limitation of using BLE to form multi-hop networks, and propose possible solutions, such as topology limitation, changes to BLE stack or hardware. This paper is a clear and quantitative problem statement on the limitations of using BLE in multi-hop networks.
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All authors Bozheng Pang, José Alamos, Thomas C. Schmidt, Matthias Wählisch

Networking

WiFi Backscatter for Green Internet of Things: Concepts, Research Trends, and Practical Challenges

Weiqi Wu, Shuo Wang and Zhaoyuan Xu

WiFi backscatter has emerged as a promising technology for green Internet of Things (IoT) connectivity by enabling battery-free devices to communicate through widely available WiFi signals. Despite substantial progress in recent years, a considerable gap remains between research prototypes and practical deployment. This paper provides an overview of WiFi backscatter from the perspective of practical and green IoT systems. We first introduce the fundamentals and key enabling techniques, together with potential IoT applications. We then outline recent research trends toward higher throughput, concurrent communication, simplified deployment, commercial compatibility, and joint communication and sensing. Furthermore, we identify the key challenges that still hinder practical deployment, such as limited transmitter-to-tag operating range and packet loss in frequency-shifted backscatter. We believe that addressing these challenges will be critical to enabling WiFi backscatter to become a practical communication technology for future green IoT systems.
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Networking

CoRF: Cross-Scene RF Synthesis by Learning Propagation and Preserving Array Physics

Kang Yang, Duaa Nakshbandi, Wan Du et al. (+1 more)

Existing radio-frequency (RF) neural fields fit each scene separately, making new-scene deployment measurement- and optimization-intensive. This work studies amortized cross-scene spatial spectrum synthesis, where a shared model learns propagation across scenes and instantiates an unseen scene from sparse target-scene measurements without scene-specific training. To achieve this, CoRF separates learned scene-dependent propagation from the known receiver-array observation model. An unordered set of spectrum-only references conditions a canonical anchor field, producing arrival directions and query-dependent component powers for each query. Analytic array physics maps these components to the receiver covariance and then to the spatial spectrum. This factorization keeps the pretrained propagation model frozen, enabling synthesis at arbitrary query locations after a single reference-conditioning pass. A necessary local reference-capacity bound and an error decomposition further characterize the formulation. Across 35 simulated scenes spanning seven categories, CoRF outperforms the strongest baseline by 7.09 dB PSNR on unseen variants of represented scene categories and by 6.97 dB on entirely unseen scene categories.
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All authors Kang Yang, Duaa Nakshbandi, Wan Du, Mani Srivastava

Networking · Cross-listed from cs.DC

VarioPath: Workload-Aware All-to-All Communication for PCIe GPU Clusters

Yao Fei, Jin Fang, Size Zheng et al. (+4 more)

AlltoAllv communication is a critical primitive in distributed large-model inference, particularly for mixture-of-experts (MoE) models. The growing adoption of PCIe GPU systems for cost-efficient inference makes AlltoAllv performance on these systems increasingly important. Without a dedicated scale-up interconnect (e.g., NVLink or Infinity Fabric), PCIe GPU systems carry both intra-node and inter-node traffic through the PCIe hierarchy, where concurrent transfers can contend for PCIe link bandwidth. This link contention, compounded by skewed traffic distributions and dynamic traffic demand, makes efficient AlltoAllv scheduling challenging. Existing approaches are either poorly suited to PCIe GPU systems or incur substantial schedule synthesis overhead that reduces their practicality in real-world deployments. We present VarioPath, an efficient AlltoAllv scheduling framework for PCIe GPU systems. It combines an offline topology-aware analyzer with an online demand-aware scheduler. The analyzer records contention-free transfer patterns as AlltoAllv channels and exploits topology symmetry to build a compact catalog for efficient search. The online scheduler decomposes each AlltoAllv invocation's demand across a sequence of channels, adapting to rapidly changing and skewed traffic while incurring low planning overhead. Evaluation on four platforms (up to 256 GPUs) shows average AlltoAllv speedups of 5.88x over FAST and 1.72x over DeepEP. End-to-end experiments show that VarioPath reduces Qwen3 inference latency by up to 27.2% and Wan2.1 generation latency by 6.1%.
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All authors Yao Fei, Jin Fang, Size Zheng, Gongming Zhao, Hongli Xu, Jiacheng Zhu, Zhijing Xin

Networking

HOCCL: Offloading Collective Communication from GPU Cores to Accelerate Distributed Training

Yao Fei, Gongming Zhao, Hongli Xu et al. (+5 more)

Large language model training involves massive computation on GPU streaming multiprocessors (SMs), the primary compute units of GPUs. Since SMs host specialized accelerators such as Tensor Cores, their efficient utilization is critical to training efficiency. Unfortunately, existing collective communication systems compete with computation for SMs, as they consume SMs for communication-related data movement and synchronization operations. We observe that communication can, in principle, be driven by DMA engines, thereby eliminating SM involvement in communication. Based on this insight, we propose HOCCL, a zero-SM collective communication framework consisting of three components: a stream manager, a point-to-point (P2P) executor, and a collective scheduler. The stream manager preserves operator-level temporal ordering with other GPU kernels. The P2P executor enables zero-SM point-to-point communication, while the collective scheduler orchestrates P2P transfers to maximize bandwidth. Experiments show that HOCCL preserves near-peak communication performance, achieving within 3% of the state of the art on average, while eliminating communication occupancy on nearly 10% of total GPU SMs. By freeing SM resources for computation, HOCCL improves end-to-end training throughput by up to 5%.
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All authors Yao Fei, Gongming Zhao, Hongli Xu, Jin Fang, Jiacheng Zhu, Shuo Xu, Kun Huang, Zhuolong Yu

Sunday 27 September

Networking

Type-Safe Decision Frameworks for Agentic 5G Control: A Theory-Driven Testbed Characterization of Where They Can Be Applied

Michail-Alexandros Kourtis and George Xilouris

This paper presents a theory-driven characterization of type-safe decision frameworks for the agentic control of 5G networks, where every decision must be an element of a declared option set rather than free text. Three design points are evaluated on an Open5GS/UERANSIM testbed with a closed core-policy loop, namely a hosted typed model (Jev), an open fine-tunable typed encoder (Laya), and a zero-label retrofit of a general language model (AnyJev). The proposed theoretical framework turns timeliness, type conformance, certification cost and cardinality into checkable applicability predicates, supported by an optimal act/escalate/abstain gate, an escalation-feasibility floor, a co-location stability condition, per-type conformal risk control with a certification label floor, and a type-mismatch bound. Measuring every predicate yields an applicability map from framework to 5G decision class. Type safety removes format failures but not the question: the fine-tuned typed encoder returned its training answer for 98-99.5% of changed questions, and its calibrated gate then acted wrongly on up to 80% of them, whereas the question-reading frameworks acted wrongly on at most 0.143 (Jev) and 0.137 (AnyJev) of any changed question, but were either hosted and 11-29 times slower (Jev) or reliant on an 8B language model (AnyJev).
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Networking · Cross-listed from cs.CV

Resource-Aware Parameter-Efficient Model Adaptation for Onboard High-Dimensional Data

Qiyang Zhang, Xinhao Li, Lei Shi et al. (+4 more)

Onboard satellite models often require frequent updates, but the weights adapted to earlier data distributions can quickly become outdated. However, updating large-scale model parameters in orbit presents significant challenges due to the limited uplink bandwidth of Low Earth Orbit (LEO) satellite systems, particularly for hyperspectral satellite imagery, where high-dimensional spectral-spatial inputs lead to increased model size and update costs. Existing full fine-tuning methods are thus expensive to retrain and difficult to deploy under strict communication constraints. To address this challenge, we propose NE-LoRA, a parameter-efficient adaptation framework for bandwidth-constrained onboard hyperspectral model updates. NE-LoRA combines a primary low-rank branch with a nonlinear auxiliary branch to capture both global update trends and complex spectral-spatial variations. Additionally, we introduce a differentiated training strategy for multi-matrix adapters, motivated by the asymmetric initialization and gradient dynamics of different adapter matrices. Experiments on four hyperspectral datasets and three representative backbone models demonstrate that NE-LoRA consistently outperforms LoRA-based baselines and remains competitive with, and in several cases superior to, full fine-tuning. Across the evaluated settings, NE-LoRA updates only a small fraction of the total parameters on average while preserving low deployment overhead, offering a favorable accuracy-communication trade-off for onboard hyperspectral adaptation.
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All authors Qiyang Zhang, Xinhao Li, Lei Shi, Zheng Lin, Jinfeng Wen, Ao Zhou, Shangguang Wang

Networking · Cross-listed from cs.SI

A Novel Approach for the SDIR Epidemic Model on Online Social Networks

Nguyen Hong Phuc, Duong Khanh Ly and Hoang Phi Dung

Information diffusion can be controlled by restricting or removing links (edges) in online social networks, as well as in real-world networks. To identify the most influential links to remove while minimizing diffusion, previous studies have proposed upper bounds for spreading processes in SIR and SIS models, using supermodularity and weighted matrices to identify critical links in contact networks. However, in some cases, existing upper bounds are not sufficiently tight to accurately capture the effect of important edges, as in the SDIR model of [14] (Khanh-Cho-Dung, Proceedings of 40th ICOIN, 2026). We therefore propose a tighter upper bound for controlling diffusion in the SDIR model by directly analyzing the dynamics of the two state vectors D and I in a $2N$-dimensional space. This approach yields an improved spectral-radius convergence condition and outperforms the previous method. Simulations on the synthetic Erdos-Renyi network and the real-world Haslemere dataset using a Greedy edge-deletion algorithm demonstrate its effectiveness for influence minimization on social networks.
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Networking · Cross-listed from cs.LG

StarBOA: Real-Time Mamba State-Space Unrolling for Sparse Radar Micro-Doppler in ISAC Networks

Mustafa Bora Çelik, Ceren Çelik and Orhan Gazi

In Integrated Sensing and Communications (ISAC), radar sensing must operate under chirp subsampling with up to 90% missing data. An attention-based baseline, limited to a 52~ms buffer, collapses toward maximum uniform entropy ($H=2.584$ bits) as sparsity increases, failing to capture long-range gait-cycle context. We propose StarBOA, which replaces attention with a causal Mamba state-space model that updates incrementally on a per-window basis without re-scanning past reconstructions. By maintaining a persistent state, StarBOA integrates over $100\times$ more temporal history at no additional per-step computational cost. StarBOA outperforms the baseline's published results across all sparsity levels, with SSIM gains increasing from $+0.0379$ at 50% missing data to $+0.2472$ at 90%. Each window is processed in 1.53~ms with zero lookahead, demonstrating efficient causal reconstruction under extreme chirp subsampling.
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Networking · Cross-listed from cs.PL

SafePar: Monitoring Asynchrony in Microservices

Karuna Grewal, P. Brighten Godfrey, Justin Hsu et al. (+1 more)

Modern cloud applications are built from loosely-coupled microservices that coordinate through well-defined APIs to service user requests. A single API request often triggers multiple downstream API calls, some executed sequentially and others spawned asynchronously in parallel. To certify safe and secure inter-service interactions in such applications, security and compliance teams must enforce policies not only over nested call/return structure, but also over the parallel structure of an execution: which calls may run concurrently, how many parallel branches can be spawned, and what combination of branch outcomes are allowed. However, existing runtime enforcement mechanisms typically model executions as sequential or purely nested traces, and cannot capture the parallel structure introduced by asynchronous API calls. Furthermore, since application implementations may not be accessible to security and compliance teams, the policy enforcement mechanism should be decoupled from the service implementation. We introduce SafePar, a specification and monitoring framework for policies over concurrent microservice executions. A SafePar policy constrains both the order of API calls and their series-parallel structure. To support seamless deployments, each policy is compiled into a series-parallel visibly pushdown automaton, a new model of computation we propose in this work, that drives a distributed runtime monitor implemented on top of the servicemesh layer. Our technique is blackbox and non-invasive: it requires no access or changes to the service implementation. Our experiments show that SafePar enforces rich concurrency-aware policies while incurring only millisecond-scale latency overhead.
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All authors Karuna Grewal, P. Brighten Godfrey, Justin Hsu, Umang Mathur

Saturday 26 September

Networking

Analyzing 10 Petabit/s Network Data with Accelerated Associative (Token) Arrays

Jeremy Kepner, Hayden Jananthan, LaToya Anderson et al. (+19 more)

As networks expand and become an ever more critical infrastructure to modern society the need to analyze these networks with the highest regard for privacy is essential to ensure their proper function. Depending on the level of the network layer to be analyzed, sources and destinations can be any combination of physical, logical, or persona/agentic endpoints, which requires the ability to handle diverse data. Invaluable to these analyses are mathematical tools that enable sophisticated mathematical algorithms to be expressed succinctly while achieving scalable vertical (within a compute node), horizontal (across compute nodes), and temporal (over different generations of hardware) performance. Associative (token) array mathematics and corresponding libraries is one approach that can meet these requirements. Accelerating these libraries with GPUs enables the analysis of the largest networks. The MIT/IEEE/Amazon Anonymized Network Sensing Graph Challenge provides a venue for highlighting the applicability of accelerated associative arrays for these types of problems. The D4M associative library has been implemented in a number of languages. This work benchmarks a prototype Matlab D4M GPU accelerated implementation of the Anonymized Network Sensing challenge across a wide range of CPU and GPU hardware. Scalable performance is demonstrated within and across CPU cores, CPU nodes, and GPU nodes. Horizontal scaling across multiple nodes was linear. Running on hundreds of GPU nodes simultaneously achieved a sustained processing rate sufficient to potentially analyze a 10 Petabit/s network.
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All authors Jeremy Kepner, Hayden Jananthan, LaToya Anderson, William Arcand, David Bestor, William Bergeron, Chansup Byun, Alex Bonn, Daniel Burrill, Vijay Gadepally, Michael Houle, Matthew Hubbell, Michael Jones, Piotr Luszczek, Peter Michaleas, Lauren Milechin, Julie Mullen, Andrew Prout, Albert Reuther, Antonio Rosa, Charles Yee, Alex Pentland

Networking · Cross-listed from cs.MA

Adaptive and Resilient Dual-Layer Resource Slicing for Hovering Aerial Backhaul Networks

Chuan-Chi Lai and Jen-Hsiang Li

This paper investigates adaptive and resilient dual-layer resource slicing in hovering aerial agent (HAA)-assisted backhaul networks for heterogeneous 5G/6G services, including enhanced mobile broadband (eMBB), ultra-reliable and low-latency communications (URLLC), and massive machine-type communications (mMTC). To address the complex coupling of this dual-layer architecture in non-stationary environments, we propose the resilient adaptive priority orchestration enhanced twin delayed deep deterministic policy gradient (RAPO-TD3) framework. We introduce a novel double soft-max projection mechanism to map the continuous action space into physically feasible bandwidth distributions, ensuring strict constraint adherence. Additionally, a resilient adaptive priority orchestration (RAPO) mechanism is embedded to safeguard mission-critical URLLC latency. Crucially, we establish a rigorous mathematical foundation proving that our framework ensures Lipschitz continuity and satisfies the Robbins-Monro conditions for stable asymptotic convergence. Extensive simulations under non-stationary traffic demonstrate that our RAPO-TD3 framework achieves superior performance relative to PPO, DDPG, and traditional solvers. Notably, via the RAPO mechanism, our approach maintains URLLC satisfaction levels closely approaching theoretical optima even during 500% demand surges. Furthermore, scalability evaluations indicate that sub-millisecond execution latencies strictly satisfy the 1 ms URLLC budget, demonstrating the performance efficacy of our proposed framework.
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2609.32798 · 26 Sep · 16 pages, 6 figures, and 3 tables. Accepted for publication in IEEE Transactions on Cognitive Communications and Networking. \copyright 2026 IEEE. Personal use of this material is permitted. However, permission to use this material for any other purposes must be obtained from the IEEE by sending a request to pubs-permissions@ieee.org

Abstract PDF $AGNT ×1

Networking · Cross-listed from cs.AI

Agentic Network Traffic Monitoring

Manuel Tsoukatos, Hayden Jananthan and Jeremy Kepner

As the use of agentic artificial intelligence increases in nearly every industry, there exists a widening attack surface. It is necessary to monitor agents to ensure that agents are acting in a way that is aligned with the users intent. Auditing an agent's network traffic provides a clear record of the agent interactions. This work presents a novel approach to monitoring the network traffic of agentic systems using complex valued hypersparse traffic matrices by integrating DBOS (DataBase OS), the OneSparse PostgreSQL database, and the GraphBLAS math library. To develop these concepts an agentic simulator was constructed, allowing a varying numbers of AI agents to collectively survey a virtual environment using different strategies. The resulting network traffic matrices enable easy monitoring of the AI agents.
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Networking · Cross-listed from math.PR

Equilibrium Joining Strategies for Queues in Two-Phase Random Environment

Konstantin Avrachenkov and Uri Yechiali

We study equilibrium joining strategies in an M/M/1-type queueing system with strategic customers operating in a two-phase random environment described as a continuous-time Markov process. Strategic customers, upon arrival, choose whether to join or to balk based on available information and anticipated utility, considering the trade-off between reward from service and waiting cost. Four observation scenarios are analysed: fully observable (both queue length and environment phase are disclosed to a customer upon arrival), queue-only observable, environment-only observable, and fully unobservable. In each case, equilibrium joining strategies are analysed. In the environment-only observable and fully unobservable cases, explicit solutions and equilibrium conditions are derived under rapid oscillations and under very slow transitions between environment phases.
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Networking

Toward Agentic Optical Networks: A Vision of LLM Agent-Driven Autonomous Lifecycle Management

Yao Zhang, Shengnan Li, Yuchen Song et al. (+11 more)

As optical networks continue to expand in scale, complexity, and service diversity, the implementation of automation has become essential for ensuring agility, efficiency, and reliability in lifecycle management (LCM) of optical networks. Large language model (LLM) Agent, distinguished by its progressively sophisticated capabilities in logical reasoning, adaptive decision-making, complex problem solving, and multi-task orchestration, presents great opportunities to advance network automation beyond traditional AI techniques. Nevertheless, the application of LLM Agent in optical networks remains in its early exploratory stage, challenged by the lack of multi-task coordination, high computational demands, data dependence, and reliability concerns. In this paper, we envision a conceptual roadmap toward Agentic Optical Networks (AONs) by integrating LLM Agents throughout the LCM with high-level autonomy. First, we trace the evolution from manual operations to AI-empowered frameworks and distill key technologies in Agent, providing actionable insights into leveraging its strengths for addressing practical network automation challenges. A core contribution of this paper is the proposal of a hierarchical multi-Agent framework, which is specifically developed to manage every phase in LCM of AONs, including planning, deployment, operation, maintenance, upgrade, and decommission, thereby enabling more cohesive and comprehensive automation throughout the entire lifecycle. In addition, future directions and underlying challenges are also discussed at the intersection of LLM and optical networks. By aligning the LLM Agent with the specialized requirements of AONs, this work aims to explore the potential for the evolution of optical networks moving from task-level semi-automatic execution toward lifecycle-level full autonomy.
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All authors Yao Zhang, Shengnan Li, Yuchen Song, Yidi Wang, Yue Pang, Wenbin Chen, Xiaotian Jiang, Xiao Luo, Meixia Fu, Min Zhang, Yongli Zhao, Shanguo Huang, Alan Pak Tao Lau, Danshi Wang

Friday 25 September

Networking · Cross-listed from cs.DC

Towards Simple Models of Complex SmartNICs

Robert Chang, Teng Jiang, Wonsup Yoon et al. (+3 more)

Cloud vendors push ambitious in-network processing (e.g., crypto, telemetry) onto the NIC to offload servers even as link rates climb to terabit speeds. Vendors have responded with heterogeneous SmartNICs. For example, NVIDIA BlueField-3 interposes—between the wire and the host CPUs—a line-rate eSwitch, a multithreaded Data-Path Accelerator, general-purpose ARM cores, and a sea of fixed-function accelerators. These devices are notoriously hard to program, and harder still to predict. Applications can be implemented in many ways, with each choice potentially hitting a different bottleneck. A designer ideally needs to know—cheaply, and before a line of code is written—feasible choices and their bottlenecks, and design patterns to improve performance. Our paper offers a starting point to answer these questions using what we call the ZRAM model. It pairs a platform graph of processing zones and their channels with a program graph of tasks and their traffic fractions. The application designer or a compiler chooses a placement that maps the program graph onto the platform graph. Three metrics computed directly from this mapping—capability, roofline, and capacity—score the placement, deciding its feasibility and naming the bottleneck resource. We use a DDoS detector as a primary case study, and briefly explore two other applications, decision-tree inference and RDMA traversal. We distill seven design patterns for programming SmartNICs including a key one we call sifting. ZRAM generalizes to other SmartNICs such as Intel IPU E2200 and AMD Pensando Salina 400, and opens a new research agenda that includes compilers and hardware design.
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All authors Robert Chang, Teng Jiang, Wonsup Yoon, Daehyeok Kim, Sam Kumar, George Varghese

Networking · Cross-listed from cs.AI

CSI-Agent: LLM-Assisted Few-Shot Adaptation for Cross-Domain Wi-Fi CSI Sensing

Tianya Zhao, Chuan Liu and Xuyu Wang

Wi-Fi channel state information (CSI) has enabled device-free sensing applications such as human activity recognition. However, CSI sensing models remain brittle in cross-domain deployment, where changes in users or environments can produce incorrect predictions. Existing solutions usually treat this problem as an offline model-design problem, by pretraining a stronger representation or applying one fixed adaptation method to the entire target domain. In practice, labeled target data are scarce and different classes may fail in different ways under the same domain shift. To address this, we propose CSI-Agent, an evidence-seeking LLM agent that reformulates cross-domain CSI adaptation as a deployment-time decision-making problem. Rather than processing raw CSI or making sample-level predictions, CSI-Agent summarizes target-domain behavior into sensing-grounded class-level evidence. It establishes a strong target-adaptive default from complementary CSI views and uses an LLM planner to determine whether each class should retain the default or invoke a specialized action. Deterministic verification and bounded execution further reduce unreliable interventions. We evaluate CSI-Agent on four public datasets using five cross-domain splits covering device, user, environment, and compositional shifts. Under 1-shot adaptation, CSI-Agent achieves the best target-domain performance across all splits and improves the average Macro-F1 by about 16% compared to the strongest baseline method.
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Networking

AirLog: Store-Level Indoor Life Logging Made Easy

Zihui Yun, Jiaying Du, Yue Yu et al. (+4 more)

This paper presents AirLog, a smartphone-based life journaling system that automatically reconstructs users' store visits in shopping malls and summarizes them into human-readable journals. Unlike conventional indoor localization systems, AirLog avoids labor-intensive radio-map construction and dedicated wireless localization infrastructure and algorithm calibrations. Instead, it repurposes two cues already available in commercial spaces: semantic information exposed by ambient Wi-Fi SSIDs and indoor directory images. AirLog converts directory images into spatial maps and fuses Wi-Fi semantic anchors with inertial dead reckoning to recover store-level trajectories, which are then summarized into journals by an LLM. Such store-level life logs can support applications such as personal memory recall, activity reflection, and automated diary generation without requiring users to manually record where they have been. We implement AirLog on commodity smartphones and evaluate it on both a large-scale public dataset and a self-collected dataset. The results demonstrate that AirLog substantially improves store-level region recovery, semantic matching, trajectory reconstruction, and journal quality over existing baselines. A human evaluation further shows that the generated journals are coherent and faithful to users' visits.
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All authors Zihui Yun, Jiaying Du, Yue Yu, Zhewei Liu, Zhen Xiang, Longfei Shangguan, Zhenlin An

Networking · Cross-listed from cs.DC

Bandwidth, Latency, and 400 Million Kilometers: The Case for Mars-Local Compute

Maleeha Masood, Indranil Gupta and Deepak Vasisht

There have been recent proposals for human settlements on Mars in 2030s. Any human activity on Mars must be preceded by extensive robotic exploration. However, Mars exploration is bottlenecked by the low bandwidth, intermittent Mars-Earth link. For example, HiRISE, a high-resolution camera onboard the Martian orbiter MRO imaged less than 3% of Mars over eleven years, even though MRO's low resolution Context Camera had mapped more than 99% of Mars in that time. We present a systems case for shared compute for Mars exploration. Such Mars-local compute, paired with advances in computer vision and AI, can enable large volumes of data to be collected and processed on Mars while sending periodic updates, insights, and selective datasets to Earth. To overcome the lack of surface infrastructure on Mars, we propose a two-tier in-orbit deployment of computational satellites that provides consistent coverage and bandwidth. Our analysis shows that the proposed deployment can start small: one areostationary node makes compute reachable from all active Mars missions, two additional areostationary nodes can extend this coverage to roughly 90% of the planet, while low-Mars-orbit nodes add high-rate surface links and compute capacity where demand grows.
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Networking

Can You Check That? The Checkability Boundary for Local LLM Network Automation

Maleeha Masood and Momina Nofal

Sending every network-automation input to a third-party frontier LLM exports sensitive artifacts such as production configurations, topologies, and logs. Querying small language models (SLMs) locally avoids this egress, but SLM outputs can be error-prone for direct use. This work introduces checkability as a criterion for determining which tasks are suitable for local inference. A task is checkable when it exposes a cheap, deterministic test - an intrinsic check - that rejects outputs violating a necessary correctness condition. We instantiate this idea in Touchstone, a local-first pipeline that uses seven off-the-shelf SLMs (1-8B parameters) to generate candidates, uses task-specific intrinsic checks to reject responses, and escalates unresolved inputs to a frontier LLM. On conflict detection and intent translation tasks, Touchstone reaches 98.6% and 93.8% end-to-end accuracy while escalating only 16% and 17% of inputs, respectively. On TeleQnA, a knowledge-only control that has no task-specific intrinsic checks, Touchstone is unable to match the accuracy of the frontier baseline. Our results support a simple deployment rule: keep inference local when task semantics support precise, low-cost checks; escalate the rest.
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Networking · Cross-listed from cs.CR

Fast and Secure Simultaneous Authentication of Equals for WPA3

João Ferreira, André Zúquete and Hélder Gomes

The Simultaneous Authentication of Equals (SAE) protocol, introduced in WPA3, provides robust protection against offline dictionary attacks against a network Pre-Shared Key (PSK) and also protection of session keys from other people knowing that PSK. However, its high computational cost for the Password Element (PE) derivation makes Access Points (APs) vulnerable to CPU exhaustion Denial-of-Service (DoS) attacks. This paper proposes a resilient architectural modification to SAE. First, we introduce an asymmetric resource cost model that offloads the iterative discovery of cryptographic elements to the client, allowing the AP to maintain a fixed computational load during the handshake. To further mitigate brute-force attempts, we implement a mechanism based on a slow-path key derivation (with variable cost key derivation functions, such as PBKDF2 or Argon2), incorporating a deliberate processing delay on the supplicant. Finally, we introduce a ticket-based mechanism to facilitate efficient re-authentication for known devices, bypassing expensive exchanges while preserving system availability under adversarial conditions. Experimental results demonstrate that this architecture significantly mitigates DoS risks without compromising legitimate network access.
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Networking

Evaluation of Acoustic Noise Level and Impulsiveness Inside Vehicles in Different Traffic Conditions

Daniel Flor, Danilo Pena, Hyago Lucas Oliveira et al. (+4 more)

Recently, the issue of sound quality inside vehicles has attracted interest from both researchers and industry alike due to health concerns and also to increase the appeal of vehicles to consumers. This work extends the analysis of interior acoustic noise inside a vehicle under several conditions by comparing measured power levels and two different models for acoustic noise, namely the Gaussian and the alpha-stable distributions. Noise samples were collected in a scenario with real traffic patterns using a measurement setup composed of a Raspberry Pi Board and a microphone strategically positioned. The analysis of the acquired data shows that the observed noise levels are higher when traffic conditions are good. Additionally, the interior noise presented considerable impulsiveness, which tends to be more severe when traffic is slower. Finally, our results suggest that noise sources related to the vehicle itself and its movement are the most relevant ones in the composition of the interior acoustic noise.
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All authors Daniel Flor, Danilo Pena, Hyago Lucas Oliveira, Luan Pena, Vicente A. de Sousa, Jr., Allan Martins

Networking

ITS Fairy: Occlusion Assistance Selected Against a Recipient's Own Perception Reports

Yenan Wang, Oscar Karlsson, Elad Michael Schiller et al. (+2 more)

Cooperative perception can expose object state beyond a vehicle's onboard sensors, but sensing occlusion can still leave a local safety application without the objects its collision computation needs. To tackle this challenge, we present the ITS Fairy, an infrastructure-side Server Local Dynamic Map (S-LDM) service whose decision unit is the pair (recipient, missing conflict-relevant object): among objects absent from a recipient's CPM-derived reported awareness, it sends only those relevant to a Time of Closest Approach (TCA) conflict test. Comparable services predict what a vehicle can perceive; the ITS Fairy instead reads what it has already reported. The recipient inserts the selected state into its local LDM and uses its unchanged collision-avoidance controller. We evaluate this application-level mechanism in SUMO–ms-van3t–S-LDM emulation, since extended as VaN3Twin, using a sensing-occluded lane merge and four-way intersection scenario. At every main-sweep speed, the smallest assisted per-encounter minimum TCA exceeds the largest local-only value in the archived data. Additionally, assisted medians remain in the multi-second range where local-only operation repeatedly approaches zero. In the lane-merge robustness data, the median benefit persists at 80% configured assistance omission with 10 and 5 Hz analysis, but largely disappears at 100-120 km/h when 80% omission is combined with 1 Hz analysis. These results demonstrate the application-level value of supplying object state selected against what a recipient has itself reported. They are not a vehicular wireless-channel evaluation, and they do not quantify what selectivity saves relative to forwarding every nearby object.
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All authors Yenan Wang, Oscar Karlsson, Elad Michael Schiller, Francesco Raviglione, Claudio Casetti

Networking

Solution for Interference in Hotspot Scenarios Applying Q-Learning on FFR-Based ICIC Techniques

Iago Diógenes do Rego and Vicente A. de Sousa Jr

This work explores interference coordination techniques (inter-cell interference coordination, ICIC) based on fractional frequency reuse (FFR) as a solution for a multi-cellular scenario with user concentration varying over time. Initially, we present the problem of high user concentration along with their consequences. Next, the use of multiple-input multiple-output (MIMO) and small cells are discussed as classic solutions to the problem, leading to the introduction of fractional frequency reuse and existing ICIC techniques that use FFR. An exploratory analysis is presented in order to demonstrate the effectiveness of ICIC techniques in reducing co-channel interference, as well as to compare different techniques. A statistical study was conducted using one of the techniques from the first analysis in order to identify which of its parameters are relevant to the system performance. Additionally, another study is presented to highlight the impact of high user concentration in the proposed scenario. Because of the dynamic aspect of the system, this work proposes a solution based on machine learning. It consists of changing the ICIC parameters automatically to maintain the best possible signal-to-interference-plus-noise ratio (SINR) in a scenario with hotspots appearing over time. All investigations are based on ns-3 simulator prototyping. The results show that the proposed Q-Learning algorithm increases the average SINR from all users and hotspot users when compared with a scenario without Q-Learning. The SINR from hotspot users is increased by 11.2% in the worst case scenario and by 180% in the best case
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Networking

Intent2Tc: Automated Intent-to-Traffic Control Translation with Language Models

Andrea Masini, Sudipta Acharya, Paolo Bellavista et al. (+2 more)

Automated and highly usable Quality-of-Service (QoS) enforcement requires translating high-level service intents into deployable traffic-management policies. Although intent-based networking (IBN) has simplified policy specification, bridging the gap between business-level intents and executable network configurations remains complex, error-prone, and difficult to automate. This paper presents Intent2Tc, a closed-loop language-model-driven framework that translates business-level traffic-shaping intents into declarative sub-intents and subsequently into validated, executable Linux traffic control (tc) configurations. The framework integrates an Active Queue Management (AQM)-based digital twin (DT) semantic model, automated metadata extraction, critique-driven refinement, and Retrieval-Augmented Generation (RAG)-based knowledge reuse to improve semantic consistency and configuration reliability. We evaluate multiple open-source large language models (LLMs) and small language models (SLMs), together with Claude Sonnet-4.6, on 100 Request for Comments (RFC) 9315-compliant traffic-shaping intents. Across both translation stages, Intent2Tc achieves high semantic fidelity, configuration accuracy, and deployment readiness, with Claude Sonnet-4.6 reaching 0.98 semantic similarity, 1.0 semantic unit coverage, and 0.045 normalized edit distance. Furthermore, RAG reduces token consumption and inference latency while enabling compact models such as Phi-4-mini to approach the performance of substantially larger models. Linux tc serves as the target configuration platform, demonstrating the practical applicability of the proposed framework.
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All authors Andrea Masini, Sudipta Acharya, Paolo Bellavista, Luca Foschini, Burak Kantarci

Networking

Dynamic Sampling for Telemetry in Microservices: A Reinforcement Learning and Entropy-Based Approach

Renan Martins Alves, Jéferson Campos Nobre and Juliano Araujo Wickboldt

Microservices architectures are increasingly deployed in cloud-based distributed environments, making application development and maintenance more dynamic, but also increasing the complexity of troubleshooting and observability. Distributed tracing tools are therefore essential for request analysis and debugging, despite introducing additional overhead that can be amplified by excessive and inefficient data collection. This article proposes RADAR (Reinforcement Learning Agent for Dynamic And Relevant trace sampling), an agent that combines reinforcement learning with a data entropy assessment to achieve more efficient capture of traces relevant to system monitoring, based on the OpenTelemetry standard. RADAR tests different sampling rules to discover which combination is most efficient. A test environment simulating a minimalist online store with several microservices distributed across a Kubernetes cluster served as the basis for the experiments, which evaluated the agent's convergence and the system's performance in terms of resource consumption and collected data quality. Results showed that RADAR reduced network bandwidth consumption by 97.4% and CPU usage by 99.0% compared to full data collection, also outperforming a fixed-rate sampling baseline. Beyond these resource savings, the approach preserved observability of critical scenarios, retaining approximately 85.6% of rare trace patterns and increasing the average entropy of the stored information by approximately 25%, validating the feasibility of using entropy to orchestrate telemetry autonomously and efficiently.
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