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

V
V2EX
C
Check Point Blog
博客园_首页
B
Blog
D
Docker
U
Unit 42
量子位
I
InfoQ
有赞技术团队
有赞技术团队
Martin Fowler
Martin Fowler
GbyAI
GbyAI
L
LangChain Blog
云风的 BLOG
云风的 BLOG
博客园 - Franky
美团技术团队
T
The Blog of Author Tim Ferriss
阮一峰的网络日志
阮一峰的网络日志
月光博客
月光博客
Vercel News
Vercel News
Recent Announcements
Recent Announcements
雷峰网
雷峰网
大猫的无限游戏
大猫的无限游戏
小众软件
小众软件
Google DeepMind News
Google DeepMind News

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
Interlaced: Fully decentralized churn stabilization for S...
Yahya Hassanzadeh-Nazarabadi, Alptekin Küpçü, Öznur Özkasap · 2019-03-18 · via cs.DC updates on arXiv.org

As a distributed hash table (DHT) routing overlay, Skip Graph is used in a variety of peer-to-peer (P2P) systems including cloud storage, social networks, and search engines. The overlay connectivity of P2P systems is negatively affected by the arrivals and departures of nodes to and from the system that is known as churn. Preserving connectivity of the overlay network (i.e., the reachability of every pair of nodes) under churn is a performance challenge in every P2P system including the Skip Graph-based ones. The existing decentralized churn stabilization solutions that are applicable on Skip Graphs have intensive communication complexities, which leave them unable to provide a strong overlay connectivity, especially under high rates of churn. In this paper, we propose Interlaced, a fully decentralized churn stabilization mechanism for Skip Graphs that provides drastically stronger overlay connectivity without changing the asymptotic complexity of the Skip Graph in terms of storage, computation, and communication. We also propose the Sliding Window De Bruijn Graph (SW-DBG) as a tool to predict the availability of nodes with high accuracy. Our simulation results show that in comparison to the best existing DHT-based solutions, Interlaced improves the overlay connectivity of Skip Graph under churn with the gain of about 1.81 times. A Skip Graph that benefits from Interlaced and SW-DBG is about 2.47 times faster on average in routing the queries under churn compared to the best existing solutions. We also present an adaptive extension of Interlaced to be applied on other DHTs, for example Kademlia.