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

人人都是产品经理
人人都是产品经理
量子位
月光博客
月光博客
罗磊的独立博客
宝玉的分享
宝玉的分享
博客园_首页
酷 壳 – CoolShell
酷 壳 – CoolShell
奇客Solidot–传递最新科技情报
奇客Solidot–传递最新科技情报
WordPress大学
WordPress大学
博客园 - 叶小钗
博客园 - 聂微东
阮一峰的网络日志
阮一峰的网络日志
V
V2EX
雷峰网
雷峰网
博客园 - 三生石上(FineUI控件)
Jina AI
Jina AI
OSCHINA 社区最新新闻
OSCHINA 社区最新新闻
博客园 - Franky
美团技术团队
爱范儿
爱范儿
V
Visual Studio Blog
让小产品的独立变现更简单 - ezindie.com
让小产品的独立变现更简单 - ezindie.com
H
Hackread – Cybersecurity News, Data Breaches, AI and More
Y
Y Combinator 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
Walrus: An Efficient Decentralized Storage Network
[Submitted on 8 May 2025 (v1), last revised 10 Aug 2026 (this ve · 2025-05-09 · via cs.DC updates on arXiv.org

View PDF

Abstract:Decentralized storage faces a fundamental trade-off between replication overhead, recovery efficiency, and security guarantees. Current approaches either rely on full replication, incurring substantial storage costs, or employ erasure-coding schemes that struggle with efficient recovery, especially under high churn. We present Walrus, a decentralized blob storage system that addresses these limitations through multiple technical innovations.
At the core of Walrus is Red Stuff, a two-dimensional erasure-coding protocol that achieves high security with only a 4.5x replication factor, while providing self-healing of lost data. This means that recovery is done without centralized coordination and requires bandwidth proportional to the amount of lost data.
However, Red Stuff on its own is not sufficient for Walrus, as it is designed with a static set of participants in mind. To further support decentralization, we also introduce a multi-stage epoch-change protocol that efficiently handles storage node churn while maintaining uninterrupted availability during committee transitions. Our system incorporates authenticated data structures to defend against malicious clients and ensure data consistency throughout storage and retrieval. Walrus has been deployed in production since March 2025 and has secured 686 TB of data by July 2026. We conduct an experimental evaluation of the deployed system and demonstrate that Walrus achieves practical performance at scale and outperforms the Arweave decentralized storage system.

Submission history

From: Markus Legner [view email]
[v1] Thu, 8 May 2025 16:06:41 UTC (385 KB)
[v2] Mon, 9 Jun 2025 16:08:54 UTC (342 KB)
[v3] Fri, 20 Feb 2026 16:24:40 UTC (268 KB)
[v4] Mon, 10 Aug 2026 12:49:12 UTC (237 KB)