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DUAL-BLADE: Dual-Path NVMe-Direct KV-Cache Offloading for Edge LLM Inference Progressive Semantic Communication for Efficient Edge-Cloud Vision-Language Models Efficient, VRAM-Constrained xLM Inference on Clients Folding Tensor and Sequence Parallelism for Memory-Efficient Transformer Training & Inference DORA: A Scalable Asynchronous Reinforcement Learning System for Language Model Training AMMA: A Multi-Chiplet Memory-Centric Architecture for Low-Latency 1M Context Attention Serving RaMP: Runtime-Aware Megakernel Polymorphism for Mixture-of-Experts Spark Policy Toolkit: Semantic Contracts and Scalable Execution for Policy Learning in Spark Internet of Everything in the 6G Era: Paradigms, Enablers, Potentials and Future Directions PolyKV: A Shared Asymmetrically-Compressed KV Cache Pool for Multi-Agent LLM Inference A Survey on Split Learning for LLM Fine-Tuning: Models, Systems, and Privacy Optimizations ITAS: A Multi-Agent Architecture for LLM-Based Intelligent Tutoring Latency and Cost of Multi-Agent Intelligent Tutoring at Scale TACO: Efficient Communication Compression of Intermediate Tensors for Scalable Tensor-Parallel LLM Training FreeScale: Distributed Training for Sequence Recommendation Models with Minimal Scaling Cost CommFuse: Hiding Tail Latency via Communication Decomposition and Fusion for Distributed LLM Training A Taxonomy and Resolution Strategy for Client-Level Disagreements in Federated Learning Usable Agent Discovery for Decentralized AI Systems Cloud to Edge: Benchmarking LLM Inference On Hardware-Accelerated Single-Board Computers Data-Free Contribution Estimation in Federated Learning using Gradient von Neumann Entropy Shard the Gradient, Scale the Model: Serverless Federated Aggregation via Gradient Partitioning Promoting Simple Agents: Ensemble Methods for Event-Log Prediction GraphLeap: Decoupling Graph Construction and Convolution for Vision GNN Acceleration on FPGA AGNT2: Autonomous Agent Economies on Interaction-Optimized Layer 2 Infrastructure FedSIR: Spectral Client Identification and Relabeling for Federated Learning with Noisy Labels Stream-CQSA: Avoiding Out-of-Memory in Attention Computation via Flexible Workload Scheduling A Delta-Aware Orchestration Framework for Scalable Multi-Agent Edge Computing Federated Learning over Blockchain-Enabled Cloud Infrastructure Optimal Routing for Federated Learning over Dynamic Satellite Networks: Tractable or Not? Sherpa.ai Privacy-Preserving Multi-Party Entity Alignment without Intersection Disclosure for Noisy Identifiers
Enabling Time-Aware Priority Traffic Management over Dist...
Alberto Scionti, Paolo Savio, Francesco Lubrano, Federico Stiran · 2025-09-30 · via cs.DC updates on arXiv.org

Network Interface Cards (NICs) greatly evolved from simple basic devices moving traffic in and out of the network to complex heterogeneous systems offloading host CPUs from performing complex tasks on in-transit packets. These latter comprise different types of devices, ranging from NICs accelerating fixed specific functions (e.g., on-the-fly data compression/decompression, checksum computation, data encryption, etc.) to complex Systems-on-Chip (SoC) equipped with both general purpose processors and specialized engines (Smart-NICs). Similarly, Field Programmable Gate Arrays (FPGAs) moved from pure reprogrammable devices to modern heterogeneous systems comprising general-purpose processors, real-time cores and even AI-oriented engines. Furthermore, the availability of high-speed network interfaces (e.g., SFPs) makes modern FPGAs a good choice for implementing Smart-NICs. In this work, we extended the functionalities offered by an open-source NIC implementation (Corundum) by enabling time-aware traffic management in hardware, and using this feature to control the bandwidth associated with different traffic classes. By exposing dedicated control registers on the AXI bus, the driver of the NIC can easily configure the transmission bandwidth of different prioritized queues. Basically, each control register is associated with a specific transmission queue (Corundum can expose up to thousands of transmission and receiving queues), and sets up the fraction of time in a transmission window which the queue is supposed to get access the output port and transmit the packets. Queues are then prioritized and associated to different traffic classes through the Linux QDISC mechanism. Experimental evaluation demonstrates that the approach allows to properly manage the bandwidth reserved to the different transmission flows.