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

The Cloudflare Blog
L
LangChain Blog
WordPress大学
WordPress大学
V
V2EX
M
MIT News - Artificial intelligence
H
Hackread – Cybersecurity News, Data Breaches, AI and More
Stack Overflow Blog
Stack Overflow Blog
J
Java Code Geeks
F
Fortinet All Blogs
钛媒体:引领未来商业与生活新知
钛媒体:引领未来商业与生活新知
腾讯CDC
freeCodeCamp Programming Tutorials: Python, JavaScript, Git & More
D
Docker
Recent Announcements
Recent Announcements
GbyAI
GbyAI
博客园 - 叶小钗
小众软件
小众软件
Hugging Face - Blog
Hugging Face - Blog
T
The Blog of Author Tim Ferriss
人人都是产品经理
人人都是产品经理
Engineering at Meta
Engineering at Meta
Y
Y Combinator Blog
雷峰网
雷峰网
The GitHub Blog
The GitHub Blog

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
Technique Report: Near-Optimal Routing Protection for SDN...
Huawei Huang, Song Guo, Weifa Liang, Keqiu Li, Baoliu Ye · 2016-03-05 · via cs.DC updates on arXiv.org

Software Defined Networking (SDN) brings numbers of advantages along with many challenges. One particular concern is on the control-plane resilience, while the existing protection approaches proposed for SDN networks mainly focus on data-plane. In order to achieve the carrier-grade recovery from link failures, we adopt the dedicated protection scheme towards finding optimal protection routing for control-plane traffic. To this end, we study a weighted cost minimization problem, in which the traffic load balancing and flow table rule placement are jointly considered when selecting protection paths for controller-switch sessions. Because this problem is known as NP-hard, we propose a Markov approximation based combinatorial optimization approach for routing protection in SDN control-plane, which produces near-optimal solution in a distributed fashion. We then extend our solution to an on-line case that can handle the single-link failure one at a time. The induced performance fluctuation is also analyzed with theoretical derivation. Extensive experimental results show that our proposed algorithm has fast convergence and high efficiency in resource utilization.