ALT Today, data center networks run multiple congestion control algorithms (CCAs), each tailored to distinct traffic characteristics, from latency-sensitive intra-region RPCs to high-throughput cross-region AI workloads. The rapid evolution of AI training and inference further accelerates this trend by continually introducing new traffic characteristics that demand new CCAs. This paper introduces CCA-as-a-Service (CCAaaS), our eBPF-based ecosystem for the rapid design, deployment, and operation of production-grade CCAs at hyperscale. We present (1) a modular BPF-TCP framework and general lessons for designing modular CCAs; (2) a deployment and orchestration layer that provides disruption-free CCA updates, along with best practices for managing a zoo of CCAs at scale; and (3) a lightweight observability tool for CCAs and a blueprint for scaling CCA observability. CCAaaS manages over 97% of our server-to-server traffic using three active eBPF-based CCAs; two of the five CCAs we have built ha

ALT Coexistence between non-terrestrial networks (NTNs) and terrestrial networks (TNs) is essential for future wireless systems, but terrestrial downlink transmissions can generate harmful aggregate interference at low-Earth orbit (LEO) satellite receivers. We present DT-STABLE, an Open Radio Access Network (O-RAN)-enabled, digital twin (DT)-assisted framework for context-aware NTN-TN coexistence. Using site-specific information, the DT estimates interference at LEO satellites, enabling proactive management with reduced real-time measurement overhead. We formulate joint beam activation and user association under RF-chain limits, load balancing, and aggregate satellite interference constraints; prove NP-hardness; and develop an efficient DT-guided scheduling algorithm. DT-STABLE couples short-timescale radio resource adaptation with periodic mid- and long-timescale DT updates through standardized O-RAN interfaces. Site-specific 28 GHz simulations under worst-case satellite geometry and low

ALT This paper studies interactions between clean-label poisoning attack and defense in vehicular networks using federated learning (FL). Basic Safety Message (BSM) classification for anomaly detection is modeled as a distributed learning problem over a vehicular network with dynamic topology. As a critical threat to FL, a feature-space clean-label attack is introduced, wherein benign samples are perturbed toward malicious feature extremes, degrading class separability while preserving labels. To counter this, a feature-consistency relabeling defense is proposed to detect and relabel suspicious samples. A game-theoretic framework is developed to capture attack-defense interactions and characterize equilibrium strategies based on attack effectiveness and cost parameters. Results show that the attack scales effectively with compromised nodes, while the proposed defense keeps attack success low without degrading clean accuracy and shifts equilibrium regions toward attack-averse regimes.

ALT Many different programmable network devices and domain-specific languages (DSLs) for programming them have been developed in recent years. However, the affiliated compiler infrastructure for each DSL has not kept up, leading to unreliable compilation results and ultimately a poor experience for developers. In response to this fragmented ecosystem, we propose an intermediate representation, Peregrine, to consolidate the common compiler functionalities within this domain into a shared, open-source infrastructure. We designed Peregrine to provide formal guarantees from the beginning, rather than adding them in a later step. The language comes with a formal semantics, along with an equivalence checker written and proven correct in Rocq. Experimental results show that Peregrine is general enough to express multiple applications written in various state-of-the-art network programming languages. The equivalence checker designed in Peregrine can be used for validating the correctness of a rang

ALT Multiagent deterministic GLP (madGLP) runs Grassroots Logic Programs (GLP) as agents that communicate solely by asynchronous message passing, and assumes fair delivery: every message sent is eventually delivered. Each agent runs on a person's smartphone, named by the person's public key. A grassroots platform depends on no global resource but the network, so agents must reach one another over Bluetooth Low Energy, on a local network, and across the Internet from behind NAT, via rendezvous servers anyone can run. What madGLP requires of such a network, and what the network requires of the platform, has not been specified. We specify the networking layer beneath madGLP. Anything that can be done in GLP is done in GLP: the layer provides only what GLP cannot reach - the network stack, the location service, application attestation, and cryptographic primitives. Three transport underlays share one interface. Over PAN and LAN, an agent advertises a rotating recognition token derived from its

ALT We analyze HTML content and hyperlinks from an offline Tor crawl containing 383 onion hosts and 2,067 directed cross-host links. The study combines semantic analysis with degree-controlled quantum–classical link prediction. Two directory groups emit 94.8% of the observed host links. Directed preferential attachment reaches ROC-AUC 0.976 with random non-edges, but falls to 0.550±0.014 under source-conditioned target-degree matching. With disjoint training and test pairs, multimodal logistic regression reaches 0.672±0.021, GAE 0.899±0.016, and VGAE 0.906±0.016. A four-qubit variational quantum classifier (VQC) comparison tests two encodings across 20 disjoint-pair graph splits under exact and 1,024-shot simulation, with classical controls using the same inputs and training examples. Its best exact mean ROC-AUC is 0.549±0.023, compared with 0.551±0.016 for RBF SVC and 0.573±0.029 for a 16-parameter MLP on the same inputs and training examples. These results support predictive signal in co

ALT Open Radio Access Network (O-RAN) enables mobility-management xApps, but a learned controller is not necessarily the most effective or efficient handover policy. This paper compares standard Event A3, a resource- and mobility-aware heuristic, and a Double Deep Q-Network (DDQN) through a common xApp-oriented interface and paired system-level simulation. The heuristic combines radio quality, target-cell resources, UE mobility, and service class, with weights optimized offline using NSGA-II, while DDQN-Multi uses temporal radio and resource observations. All controllers are evaluated on identical UE trajectories, traffic assignments, and ray-traced radio inputs under Base, High-Mobility, and High-UE scenarios. Performance is assessed using service, handover, resource, energy, and fairness KPIs, complemented by CRITIC–TOPSIS ranking and complexity analysis. Results show that the heuristic achieves the lowest outage in all three scenarios and ranks first in the CRITIC–TOPSIS comparison, whi

ALT Concurrent applications (xApps) in the Near-Real-Time RAN Intelligent Controller (Near-RT RIC) of the Open Radio Access Network (O-RAN) can issue control intents that are jointly infeasible when they act on hierarchically coupled radio resources. This paper experimentally studies an interaction between slice-level physical resource block (PRB) allocation and per-user-equipment (UE) scheduling. We implement a utilisation-aware slicing control (the spectral-efficiency xApp) that reclaims a fixed fraction of the baseline allocation from under-utilised slices and redistributes pooled capacity to saturated slices. The control logic is exercised in simulation and on a 5G testbed comprising an OCUDU-based next-generation NodeB (gNB), an Open5GS core, a Universal Software Radio Peripheral (USRP) X310, and five commercial off-the-shelf (COTS) UEs. Because the gNB counters available in this testbed are UE-keyed but not slice-labelled, the slicing control derives a per-slice throughput proxy from

ALT AI-RAN platforms are expected to share CPU and GPU resources between radio workloads and AI services. Aggregate telemetry reports operating conditions, but timing changes may reflect different workloads, processors, or measurement boundaries. We propose workload-aware monitoring that associates radio-work descriptors with processing paths, timing boundaries, and operating context before comparing like with like. In 16 over-the-air downlink experiments, hybrid dispatch (CPU for transport blocks (TBs) below 4,096 bytes and GPU otherwise) has 29.66% and 22.93% lower mean host-callback durations than direct GPU dispatch, without and with a synthetic GPU co-runner, respectively. Grouping locates the separation in small TBs, while large-TB callback-duration ranges overlap. These observations identify a candidate for complementary CPU/GPU use.

ALT Radio access network (RAN) telemetry reveals radio conditions, protocol behavior, and their consequences for applications. Evaluating its usefulness requires controlled impairments and observations of the same execution. We present C and Python channel brokers and a fault injection workflow for software 5G testbeds connecting an OCUDU base station and a software UE through ZeroMQ. The brokers apply scheduled, directional transformations to I/Q samples and restore the link, while the workflow jointly records applied faults, native and programmable (codelet) telemetry, and application delay. We demonstrate this capability through 45 comparative trials of brief uplink interruptions and stepped additive noise. These use cases distinguish recognizing a radio change from measuring its service consequence: native missing-feedback events retrospectively identify interruptions, whereas noise changes decoding-error measurements without producing those events. Median on-time round-trip times rema

ALT Researchers need blockchain experimental environments with configurable workloads, controllable network conditions, and observable outcomes. Public blockchains and testnets limit experimental control, model-based simulators omit real client implementations, and real-client environments face challenges in combining network control with resource-efficient deployment. We present ChainSEED, a scalable, high-fidelity blockchain emulator built on the SEED Internet Emulator. It separates blockchain configuration from client placement on a configurable routed network. For Ethereum PoS, it organizes separately configured execution, consensus, and validator clients and provides APIs for customizing genesis state, peer connections, and client software. A transaction generator informed by Ethereum mainnet activity, application interfaces, and chain- and node-level monitoring support experiments. ChainSEED also supports Monero and Solana. We examine beacon-node memory reduction, execution-client me

ALT Unmanned Aerial Vehicles (UAVs) in Flying Networks (FNs) increasingly require mechanisms to translate high-level natural language objectives into executable missions. Although Large Language Models (LLMs) have demonstrated the feasibility of language-driven UAV control, existing approaches typically rely on static model configurations and overlook mission-aware performance variability. This paper presents AERIAL, a benchmark-driven framework for adaptive LLM selection in hybrid edge-cloud UAV control. AERIAL separates systematic model benchmarking from mission execution, deriving empirical scenario-model mappings and applying them at runtime for mission-aware selection. Evaluation across 720 automated simulation runs spanning navigation, spatial reasoning, and algorithm invocation revealed significant performance variability depending on scenario-model pairing. A case study integrating AERIAL with a FNs placement algorithm further validated the framework's ability to interface with dom

ALT Ambient Internet of Things (A-IoT) devices operate under stringent energy constraints, relying on harvested power in the μW-to-mW range and limited energy storage. The A-IoT air interface for active devices is still under development; we therefore use Narrowband Internet of Things (NB-IoT) as an implementable reference for evaluating autonomous Uplink (UL) access under harvested energy. Under these conditions, the standard NB-IoT contention-based Random Access (RA) procedure incurs substantial per-packet overhead, making UL access in dense A-IoT networks increasingly energy-expensive. Context-retaining UL access schemes reduce this overhead, but their energy-latency trade-offs are insufficiently studied in literature under realistic harvesting and protocol constraints. To address this gap, we extend the LENA-NB ns-3 module with solar energy harvesting, supercapacitor storage, and a hysteretic brown-out mechanism, and propose Hybrid Scheduling Request (HSR), a hybrid UL access scheme co

ALT Network failures degrade Multipath Reliable Connection (MRC) in direct-connect optical datacenters. We propose Optical MRC (OMRC), which remaps collectives and circuits, preserving 95.7% of training throughput, restoring stalled inference, and achieving 4.7x MRC's simulated bandwidth.

ALT Distributed quantum computing (DQC) is a promising near-term approach for executing circuits that exceed the capacity of a single quantum processing unit (QPU). Prior work has made significant progress on circuit partitioning and reconstruction, but practical DQC requires more than cutting a circuit into smaller pieces. A runtime system must determine whether to execute the circuit intact, cut it for sequential execution on one QPU, or distribute it across multiple QPUs, while accounting for queueing, communication, reconstruction, and hardware constraints. We present a resource-aware scheduler for DQC that compares these execution alternatives at runtime using explicit models of QPU execution, communication, and host-side reconstruction, together with hardware-informed timing derived from simulated IBM QPUs. By treating communication and reconstruction as first-class scheduling costs, the scheduler selects execution plans using a more realistic view of end-to-end overhead than cutting

ALT Antenna diversity in Internet of Things (IoT) devices is explored to address challenges associated with fixed device orientation and interference from nearby metallic objects, which can degrade wireless network performance. Integrating multiple antennas with different polarizations can significantly enhance signal quality and network reliability. Experiments demonstrated significant improvements in link quality, with gains exceeding 30 dB in received signal strength for specific scenarios. Already the addition of a second PCB-based antenna alone, resulted in a 17 dB improvement. These findings suggest that the added complexity in hardware and software to incorporate antenna diversity could well be justified by the resulting enhancements in network stability for real-world applications.

ALT Alarm root-cause analysis is studied on data that cannot be shared. Operator logs are confidential, so what the field publishes is a causal graph between alarm types rather than per-incident causes, a fixed scenario set, gated, or never released. Two recent benchmarks carry per-incident root causes at scale; neither states a download location or a licence. We give a generator rather than a dataset. It runs on real backbone topologies from the Internet Topology Zoo and records the exact root cause and the propagation path of every alarm – a by-product of generation, not a labelling pass. Every emitted field binds to a term in NORIA-O, RFC 8632, RFC 8345 or TMF642, and an unknown term fails the run. We ship fixed configurations, splits, a scorer and nine baselines. A one-line rule – rank the device that raised the earliest critical alarm – takes top-1 at 0.650 against 0.575 for our own temporal method, and wins 13 of 15 runs over three backbones and five seeds. An LLM given the same view

ALT Carpet bombing attacks distribute malicious traffic across many destinations within a network prefix, while operational monitoring commonly provides destination-specific telemetry for only a subset of those targets. This creates an attack-conditional observability problem that is not captured by address utilization or nominal monitoring coverage. We introduce the Prefix Visibility Ratio (PVR), defined as the fraction of attacked destinations for which the telemetry required by the host-level evidence plane is available, and argue that PVR should be treated as an explicit evaluation dimension for carpet bombing detection. We then present VANTA (Visibility-Aware Network Threat Analysis) as one concrete way to operationalize this principle. VANTA combines host-level and prefix-level anomaly scores, estimates PVR online from router-side destination evidence and the monitored-destination inventory, and adjusts evidence fusion according to both estimated visibility and estimator confidence.

ALT Pingmesh monitors datacenter networks through periodic pings between servers. In reconfigurable datacenter networks, topology engineering (TPE) and traffic engineering (TE) make uniform server-pair probing inefficient: a pinglist wastes budget retesting common paths while missing low-probability interfaces. Frequent control-plane updates further require rapid pinglist refresh, and cause path ambiguity at control-plane boundaries for fault localization. We present D-Pingmesh, a probing and fault-localization system co-designed with the TPE/TE control plane. Its control-plane-aware generator builds task-efficient pinglists by greedily admitting probes with high interface coverage under the current TPE/TE snapshot. An incremental replay-and-repair mechanism updates pinglists across reconfigurations rather than recomputing from scratch. At control-plane boundaries, a Rho-EM localizer jointly infers per-task old/new-path mixture weights and the fault set through EM-style iteration. We evalu

ALT Message Queue Telemetry Transport (MQTT) is widely used in IoT environments due to its lightweight publish-subscribe design and suitability for resource-constrained networks. While current MQTT security studies mainly focus on detecting attacks and optimizing classification performance, this work takes a different perspective by presenting a cross-dataset, tool-aware characterization of MQTT traffic at the flow level. This perspective is motivated by the fact that flow-generation is tool-dependent: flows are not directly observed in packet captures, but are constructed through tool-specific decisions about packet grouping, flow termination, and feature export. Using packet captures from 4 public MQTT datasets, we generated over 380K flows with 3 open-access flow-generation tools: Zeek-flowmeter, Argus, and NTLFlowLyzer. We analyzed the tool-specific flows using K-means, DBSCAN, and HDBSCAN, with and without PCA-based dimensionality reduction. We measured clustering quality using 2 metr