惯性聚合 高效追踪和阅读你感兴趣的博客、新闻、科技资讯
阅读原文 在惯性聚合中打开

推荐订阅源

B
Blog
A
About on SuperTechFans
Microsoft Security Blog
Microsoft Security Blog
Y
Y Combinator Blog
罗磊的独立博客
J
Java Code Geeks
人人都是产品经理
人人都是产品经理
MongoDB | Blog
MongoDB | Blog
The GitHub Blog
The GitHub Blog
G
Google Developers Blog
U
Unit 42
Microsoft Azure Blog
Microsoft Azure Blog
博客园 - Franky
Jina AI
Jina AI
F
Fortinet All Blogs
H
Help Net Security
B
Blog RSS Feed
H
Hackread – Cybersecurity News, Data Breaches, AI and More
Last Week in AI
Last Week in AI
博客园 - 司徒正美
云风的 BLOG
云风的 BLOG
M
MIT News - Artificial intelligence
C
Check Point Blog
GbyAI
GbyAI

cs.DC updates on arXiv.org

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
Single-Connection Mixed-Criticality Transport with CATS: ...
Syed Muhammad Aqdas Rizvi · 2026-06-16 · via cs.DC updates on arXiv.org

Mixed-criticality applications, such as satellite terminals, industrial telemetry, embedded systems, tactical, and other constrained links, often multiplex a small, latency-critical message class and bulk traffic over a single commodity transport connection. A single FIFO connection can starve the critical class under load. The obvious alternative, opening parallel connections, costs an additional five-tuple (often blocked by carrier-grade NAT, port budgets, and operator policy) and is not always available; when the critical class is light, two connections can also be bandwidth-fair only in aggregate rather than single-flow fair. We present CATS (Conductor-driven Asymmetric Transport Scheme), a sender-side, receiver-transparent transport-layer priority scheme over TCP: a Conductor assigns each message a priority class and just-in-time sequence numbers, using a credit-based shaper. CATS provides the one combination its alternatives cannot: deterministic non-starvation together with single-flow fairness, plus a provable bounded per-class delay. We then show that, crucially, CATS-over-TCP is not a tail-latency mechanism, and why. Three structural barriers bound in-band priority: the in-order sequence space (head-of-line blocking), the shared congestion window (cross-class coupling), and the per-flow granularity of network QoS (in-band priority is invisible to it). These barriers explain why fair-queuing and even the modern low-latency standard L4S cannot help a single connection, and why two parallel connections reduce the latency tail at the cost of an additional flow. We give CATS-over-QUIC as the principled escape: independent streams with per-stream isolation under aggregate-coupled congestion control self-isolate at the endpoint, attaining the guarantees on one fair flow. An ns-3 evaluation and QUIC proof-of-concept support the findings.