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Tue, Aug 4

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📄 paper

CENTILE: A Telemetry Foundation Model Evaluated by the Decisions It Drives

Modern computing and networking infrastructure emits telemetry continuously, yet operators convert it into decisions with a separate predictor per task, entity, and horizon. One generative model, pretrained once over an operator's own event streams, could replace this fleet, an approach that already scales to high-cardinality streams in recommendation systems. However, point-forecast error on operational telemetry saturates near simple last-value baselines, so lower error alone need not improve the decisions it feeds. To close this gap, we present \sys, a generative foundation model for network and systems telemetry, evaluated by replaying the decisions its calibrated conditional quantiles drive. \sys treats heterogeneous telemetry as event-driven, irregularly timed entity streams and serves flexible forecast horizons in a single pass, requiring no future timestamps. To our knowledge, \sys is the first pretrained telemetry model to improve both HPC scheduling and network provisioning decisions under replay, its runtime estimator transferring zero-shot across months and its pretrained weights across domains from hours of target data. Extensive experiments on HPC job logs and network traffic confirm that \sys lowers the mean bounded slowdown of backfilling by up to approximately $77\%$ over deployed user estimates and roughly halves the deployed rule's violation rate. Our code is available at https://github.com/ZzZTripleZzZ/all-in-one.

Networking advanced NetworkingInternet Architecture
By: Zifan Zhang, Zhichao Hou, Tingxiang Ji +1 more
Source: arXiv Aug 2, 2026
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10 min read
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📄 paper

Predictive Exposure and Cryptographic Readiness: A Vendor-Neutral Framework, a, Bounded Multivocal Evidence Analysis, and Reproducible Synthetic Evaluation for SD-WAN Environments

SD-WAN teams often use static severity scores to decide what to fix first. These scores do not show live exploitation, network exposure, attack paths, business impact, or cryptographic migration risk. This study asks whether a vendor-neutral PECR framework can produce a different and more traceable ranking than CVSS alone. We reviewed 19 DOI-verified publications from 2020-2026 and five current NIST documents. The review supported ten normalized factors, one weighted score, and a separate confidence measure. We then compared equal-weight PECR with CVSS in five synthetic cases. The tests used Kendall's $τ_b$, mean absolute rank shift (MARS), top-three Jaccard overlap ($J_3$), factor removal, and bounded weight changes. CVSS ranked the cases A-D-B-E-C. PECR ranked them A-B-E-C-D. The results were $τ_b = 0.40$, MARS = 1.2, and $J_3 = 0.50$. The exact $τ$ test was not significant ($p = 0.483$) because the sample had only five cases. The PECR order remained unchanged in 87.5% of 1,024 weight combinations. It also remained unchanged in eight of ten single-factor removal tests. The evidence supports separate measures for severity, exploitation, and organizational context. Evidence for live SD-WAN attack paths and daily PQC triage is still limited. PECR can produce a different and auditable ranking. This synthetic test does not prove better operational results or better human understanding.

Networking advanced NetworkingInternet Architecture
By: Saeed Alam
Source: arXiv Aug 2, 2026
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10 min read
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📄 paper

Cross-Layer Optimization and System-Level Design of Next-Generation Wireless Networks via Intelligent RAN Control

Recent years have seen the evolution of the traditional Radio Access Network (RAN) toward more open, programmable, disaggregated, and intelligent architectures, known as an Open RAN. Future Next Generation (NextG) networks are envisioned to be AI-native, enabling data-driven closed-loop optimization of Base Station resources, while Reconfigurable Intelligent Surfaces (RIS) emerge as key enablers for wireless propagation and spectral efficiency toward 6G and beyond. This dissertation focuses on the design, optimization, and experimental evaluation of NextG RANs integrating Open RAN principles, data-driven control loops, and intelligent resource allocation. The work emphasizes cross-layer optimization, including energy-efficient power control, and explores AI-driven network slicing, scheduling, and link adaptation, demonstrating NextG RANs reconfigurable in real time to meet 6G requirements, first analyzing architectural enablers and modeling frameworks, then prototyping and evaluating solutions on experimental platforms and Digital Twins. Main contributions include: (i) Deep Reinforcement Learning (DRL) solutions for network slicing and scheduling; (ii) PandORA, a framework for automatic design, training, and deployment of DRL-based Open RAN applications on the Colosseum wireless network emulator; (iii) physical-layer RIS channel modeling and optimized resource allocation across spectrum bands; (iv) system-level evaluation of RIS-assisted channels for eMBB and URLLC traffic; (v) integration of RIS within Open RAN; (vi) online RL solutions for link adaptation; and (vii) spectrum sharing between cellular and Non-Terrestrial Network links via power control and beamforming. This work provides algorithmic designs, frameworks, and validation from simulation and hardware-in-the-loop emulation to over-the-air 5G testbed experiments, addressing industry and academic needs for wireless research.

Networking advanced NetworkingInternet Architecture
By: Maria Tsampazi
Source: arXiv Aug 3, 2026
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10 min read
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📄 paper

Energy-Latency Trade-offs in O-RAN with Distributed Baseband Processing and AI Inference

The Open Radio Access Network (O-RAN) architecture introduces flexible functional splits and open interfaces that enable distributed and centralized deployment of baseband processing. While this flexibility offers opportunities for improved resource utilization, it also introduces fundamental trade-offs between energy efficiency and latency. In this paper, we develop a throughput-based end-to-end energy consumption model for O-RAN and extend it by incorporating detailed latency modeling and application-specific Artificial Intelligence/Machine Learning inference costs. The proposed end-to-end modeling framework provides a general representation of processing, transport, and inference-related energy and delay across the access, metro, and long-haul network segments. Building on this general model, we formulate an optimization problem that selects the placement of baseband processing and AI inference tasks across candidate O-RAN configurations to analyze energy-latency tradeoffs under network load, server frequency, and energy-budget constraints. Using representative hardware platforms and realistic traffic assumptions, we evaluate multiple baseband processing placements corresponding to different O-RAN functional configurations. Our results reveal how user quality of service requirements and network load conditions jointly determine the optimal placement of baseband processing and AI inference tasks, highlighting the inherent trade-off between energy efficiency and latency. The analysis provides practical insights for latency-aware and energy-efficient O-RAN deployments supporting emerging AI-driven services.

Networking advanced NetworkingInternet Architecture
By: Urooj Tariq, Rishu Raj, Shashi Raj Pandey +3 more
Source: arXiv Aug 3, 2026
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10 min read
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📄 paper

TurboRetry: Mitigating Large-Scale QUIC Handshake Floods with Off-the-Shelf DPU Offloading

The modern transport protocol QUIC is designed to enhance network performance and security, but it remains vulnerable to handshake flooding attacks. Such attacks exhaust CPU resources by forcing the server to perform expensive cryptographic operations via a large number of handshaking requests. QUIC provides a built-in defense mechanism, the Retry mechanism, to mitigate these attacks. However, our experiments reveal that it can still become a performance bottleneck under large-scale QUIC handshake floods due to substantial computational overhead. In this paper, we design and implement TurboRetry, a split design, that offloads the Retry mechanism onto DPUs to efficiently mitigate QUIC handshake floods. TurboRetry partitions the tasks of the Retry into two categories, and then assigns them to the DPUs and the host, respectively. To preserve QUIC semantics and reduce the coordination overhead, TurboRetry designs an extended Retry token format and an efficient cooperation scheme. In addition, TurboRetry offloads the connection authorization task to the on-path DPA to further improve both performance and security. Our evaluation shows that TurboRetry outperforms the host-side implementation by a wide margin, improving throughput by 10-20$\times$.

Networking advanced NetworkingInternet Architecture
By: Jiahao Wu, Heng Pan, Kai Lv +3 more
Source: arXiv Aug 3, 2026
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5 min read
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📄 paper

In-Network Market Prediction Using Machine Learning and Limit Order Books

Machine learning is significantly transforming algorithmic trading, yet the requirement for rapid execution speeds persists. While both aspects aim to boost profitability, embedding advanced machine-learning techniques with reduced trading latency presents a notable challenge. Adopting in-network machine learning, which involves offloading inference to programmable network devices, offers a delicate equilibrium in this trade-off. In this paper, we present LOBIN, a solution that utilizes machine learning within the network for market prediction based on high-frequency market data feeds. LOBIN is adept at constructing limit order books and performing inference directly within programmable switches. When compared to server-based benchmarks, LOBIN not only predicts future stock price movements with higher throughput but also maintains robust machine learning performance. It achieves over a 10% reduction in latency compared to the NASDAQ order-matching server benchmark and delivers microsecond-level latency. Furthermore, the machine learning performance of LOBIN can be further enhanced through the adoption of a hybrid deployment approach that integrates both the switch and the servers. Our evaluation demonstrates that among all data feeds of evaluated stocks, the application of hybrid deployment results in approximately 45% of the traffic and 38\% of the total potential transaction value being processed within switches without server intervention, reducing latency while ensuring that the average change in error rate of predictions remains at around 3% relative to benchmarks based solely on server use.

Networking advanced NetworkingInternet Architecture
By: Xinpeng Hong, Changgang Zheng, Joshua Lilley +2 more
Source: arXiv Aug 3, 2026
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10 min read
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📝 blog

The Future of Connectivity: Cisco Testifies Before Senate Commerce Subcommittee on AI-Native Networking

Cisco’s Bob Everson, Chief Architect for Service Provider Mobility, testified before the Senate Commerce Telecom Subcommittee about AI's impact on networks.

Networking intermediate Enterprise NetworkingNetwork Security
By: Evan Viau
Source: Cisco Blogs Aug 4, 2026
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1 min read
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📝 blog

The Full-Circle Moment: A Family Legacy at Cisco

Jackie T., PROPEL Customer Project Specialist, reflects on her family's multi-generational legacy and shares how Cisco supports her own career growth today.

Networking intermediate Enterprise NetworkingNetwork Security
By: Jackie Taylor
Source: Cisco Blogs Aug 4, 2026
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1 min read
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📝 blog

The AI invoice nobody planned for — and how Cisco is solving it from the inside

Struggling with runaway AI costs? Explore how Cisco uses observability to track, attribute, and optimize token spend across our enterprise AI operations.

Networking intermediate Enterprise NetworkingNetwork Security
By: Richard Delisser
Source: Cisco Blogs Aug 4, 2026
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1 min read
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📝 blog

Beyond the Protocol: Applying API Engineering Practices to MCP Servers

Cisco DevNet applies API engineering practices, such as versioning, linting, changelogs, documentation, to MCP servers with a new format: MCP Description.

Networking intermediate Enterprise NetworkingNetwork Security
By: Adrienne Moherek
Source: Cisco Blogs Aug 4, 2026
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1 min read
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