This paper investigates a network side-channel vulnerability in multi-tenant datacenter fabrics caused by shared congestion behavior, achieving up to 97.3% run-level accuracy in workload inference.

This paper proposes CAPS, a scheduling layer for data centers that separates rate computation and packet scheduling, reducing queue occupancy by up to 10x without throughput loss.

This paper proposes a host-driven method for flowlet balancing using Segment Routing over IPv6 (SRv6), reducing tail latency by 15% and 33% compared to random flowlet balancing and ECMP, respectively.

immUNITY is a system that enhances network security by combining programmable switches and SmartNICs to efficiently detect and mitigate low-volume and slow network attacks.

The paper proposes a two-timescale governance framework using a multi-agent LLM to safely update and guide RL agents for SDN-IoT defense, significantly improving performance and stability under advers…

The ZCube topology, which eliminates path multiplicity and reduces switching hardware, delivers better performance for large model training and inference than traditional multipath datacenter networks…

The paper proposes a federated, high-throughput stream-processing framework for cross-sector threat detection and automated containment, achieving end-to-end operational convergence within 12-20 secon…

ProcRoute is a system that restricts internal network route access to specific, authorized applications, preventing unprivileged processes from exploiting split-tunnel VPN routes.

This paper compares Google's BBR-v3 Congestion Control Algorithm to eight others over SpaceX's Starlink network, demonstrating its fairness and throughput maximization in high-latency, variable satell…

The paper introduces DEFT, a novel Mixture-of-Experts DRL architecture, to intelligently schedule dynamic cloud workflows with varying deadlines, significantly improving performance over existing sing…

The paper analyzes persistent TLS misconfigurations and introduces TLSGatekeeper, a high-performance, network-based tool that enforces security policies by monitoring TLS handshakes without requiring…

This paper proposes ARTSN, a scheduling paradigm for autonomous real-time systems using time-sensitive networking, addressing volatility and absence challenges of self-triggered traffic.

SPARK introduces a predictive, traffic-aware autoscaling toolchain for Kubernetes that uses eBPF to enhance security and significantly reduce timeout errors during sudden traffic spikes.

This paper demonstrates that a complex deep reinforcement learning policy for power grid control can be successfully distilled into a lightweight, auditable decision tree and random forest surrogate t…

This paper proposes a hierarchical analytical framework to characterize region-level latency differences in Low-Earth orbit satellite Internet using Starlink RTT measurements.

This paper presents AlphaRoute, a multi-objective adaptive search framework for VLSI global routing using Large Language Models as semantic policy optimizers.

This paper proposes a carbon-aware routing policy for geo-distributed cloud deployments, achieving up to 46.8% carbon reduction while maintaining zero SLA violations.

The paper introduces CCLab, an adversarial testing framework, to systematically evaluate the robustness of both learning-based and traditional congestion controllers, finding that learning-based contr…