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Clouds provide latency-sensitive services to clients at geographically distributed sites. They need precise control of client-site mappings for performance and, if a site fails, fast failover to other sites without cascading failures due to failover-induced overload or risky routing reconfiguration. However, clouds today route clients to sites by anycast or unicast with DNS-based redirection--methods that compromise either control or availability. In fact, even beyond these existing techniques, we find that all general-purpose approaches for directing clients to sites face inevitable tradeoffs among control, availability, and routing stability. We then present new general-purpose routing techniques and demonstrate via Internet-scale experiments that they provide much better tradeoffs among these three goals than existing techniques. One of our techniques achieves anycast's advantages with much better control, and another speeds up unicast's failover while preserving its advantages. Our techniques establish a new Pareto frontier for cloud routing. 

Multiple visions of 6G networks elicit Artificial Intelligence (AI) as a central, native element. When 6G systems are deployed at a large scale, end-to-end AI-based solutions will necessarily have to encompass both the radio and the fiberoptical domain. This paper introduces the Decentralized Multi- Party, Multi-Network AI (DMMAI) framework for integrating AI into 6G networks deployed at scale. DMMAI harmonizes AI-driven controls across diverse network platforms and thus facilitates networks that autonomously configure, monitor, and repair themselves. This is particularly crucial at the network edge, where advanced applications meet heightened functionality and security demands. The radio/optical integration is vital due to the current compartmentalization of AI research within these domains, which lacks a comprehensive understanding of their interaction. Our approach explores multi-network orchestration and AI control integration, filling a critical gap in standardized frameworks for AI-driven coordination in 6G networks. The DMMAI framework is a step towards a global standard for AI in 6G, aiming to establish reference use cases, data and model management methods, and benchmarking platforms for future AI/ML solutions. 

Given the increasing residential Internet use, a thorough understanding of what services are used and how they are delivered to residential networks is crucial. However, access to residential traces is limited due to their proprietary nature. Most prior work used campus datasets from academic buildings and undergraduate dorms, and the few studies with residential traces are often outdated or use data unavailable to other researchers. We provide access to a new residential dataset-we have been collecting traffic from ~1000 off-campus residences that house faculty, postdocs, graduate students, and their families. Although our residents are university affiliates, our dataset captures their activity at home, and we show that this dataset offers a distinct perspective from the campus and dorm traffic. We investigate the serving infrastructures and services accessed by the residences, revealing several interesting findings: peer-to-peer activity is notable, comprising 47% of the total flow duration; third-party CDNs host many services but serve much less traffic (e.g., Cloudflare hosts 19% of domains but only 2% of traffic); and 11 of the top 100 services that have nearby servers often serve users from at least 1,000km farther away. This broad analysis, as well as our data sharing, pushes toward a more thorough understanding of Internet service usage and delivery, motivating and supporting future research. 

This paper presents findings from an extensive 28 GHz mmWave measurement campaign conducted in New York City. The study includes over 20 million power measurements collected from two key scenarios: around-corner (non-line-ofsight due to building blockages) and same-street (nominally lineof-sight without obstructions from street furniture or foliage), covering over 1,300 unique links. For urban macro-cell (UMa) rooftop base stations above local clutter, the dominant angle of arrival (AoA) deviates by only 2 to 3.5 degrees from the direct transmitter/receiver direction. This small deviation allows for effective spatial separation between users, facilitating the future development of Multi-User MIMO algorithms for Beyond5G networks. In the urban micro-cell (UMi) dataset, with base stations below local clutter, a path gain drop of over 20 dB was observed in around-corner segments just 20 meters into a corner. Our Street-Clutter-NLOS path loss model achieves an RMSE of 6.4 dB, compared to 11.9 dB from NLOS 3GPP models. Using the best path loss model to estimate coverage for 90% of users traveling around corners, downlink rates could drop by over 10 times after 50 meters, highlighting the challenges in maintaining consistent user experience over mmWave networks in urban street canyons. 

The rapid advancement of deepfake technology presents significant challenges for video authenticity verification, necessitating robust detection mechanisms. This paper introduces a novel framework for deepfake video detection that integrates the Multi-Scale Spectral-Guided Graph Attention Network (MSG-GAT) with the Lightweight Assimilation-Elimination (Lite-ASEL) algorithm. By representing video frames as graphs, the framework effectively captures intricate pixel relationships, enabling the detection of subtle manipulations with enhanced precision. Additionally, the Lite-ASEL algorithm is employed for feature selection, balancing reduced computational complexity with high detection performance. Experimental results demonstrate the superiority of the proposed framework, achieving state-of-theart performance with an AUC of 99.1% and a detection accuracy of 99.3%. Furthermore, hyperparameter tuning confirms the framework’s robustness and efficiency, consistently achieving an optimal objective score of 98.54%, validating its effectiveness for optimal feature selection and deepfake detection. 

The Software-Defined Networking (SDN) paradigm has significantly improved network efficiency by integrating machine learning (ML) capabilities into the control plane (CP). This integration allows adaptive management of network resources in response to dynamic traffic conditions. However, the significant geographical distance between the CP and data plane (DP) causes considerable round-trip latency, measured in milliseconds, which poses a major challenge to time-sensitive traffic. To address this challenge, our research proposes a novel in-network Reinforcement Learning (RL) inference framework. This framework extends programmability from the CP to the DP, enabling timely and precise control of network resources to meet the stringent Quality of Service (QoS) requirements of mission-critical applications. The in-network RL inference is implemented using match-action tables within the Protocol Independent Switch Architecture (PISA) framework in the DP and validated through hardware deployment of protocol-independent packet processors (P4). To allocate bandwidth precisely based on QoS requirements, a P4 meter extern is used to differentiate the unique demands of individual traffic flows. Our enhanced deepdeterministic policy gradient (eDDPG)-based RL inference achieves superior performance with minimal processing overhead, reducing latency by 88.7% and jitter by 89.1% compared to systems without RL inference. 

Accurate modeling of the gain spectrum in erbium-doped fiber amplifiers (EDFAs) is essential for optimizing optical network performance, particularly as networks evolve toward multi-vendor solutions. In this work, we propose a generalized few-shot transfer learning architecture based on a semi-supervised self-normalizing neural network (SS-NN) that leverages internal EDFA features—such as VOA input/output power and attenuation—to improve gain spectrum prediction. Our SS-NN model employs a two-phase training strategy comprising unsupervised pre-training with noise-augmented measurements and supervised fine-tuning with a custom-weighted MSE loss. Furthermore, we extend the framework with transfer learning (TL) techniques that enable both homogeneous (same-feature space) and heterogeneous (different-feature sets) model adaptation across booster, pre-amplifier, and ILA EDFAs. To address feature mismatches in heterogeneous TL, we incorporate a covariance matching loss to align second-order feature statistics between the source and target domains. Extensive experiments conducted across 26 EDFAs in the COSMOS and Open Ireland testbeds demonstrate that the proposed approach significantly reduces the number of measurement requirements on the system while achieving lower mean absolute errors and improved error distributions compared to benchmark methods. 

In order to enable the simultaneous transmission and reception of wireless signals on the same frequency, a fullduplex (FD) radio must be capable of suppressing the powerful self-interference (SI) signal emitted from the transmitter and picked up by the receiver. Critically, a major bottleneck in wideband FD deployments is the need for adaptive SI cancellation (SIC) that would allow the FD wireless system to achieve strong cancellation across different settings with distinct electromagnetic environments. In this work, we evaluate the performance of an adaptive wideband FD radio in three different locations and demonstrate that it achieves strong SIC in every location across different bandwidths.