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

L
LangChain Blog
N
Netflix TechBlog - Medium
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
V
V2EX
Cyber Security Advisories - MS-ISAC
Cyber Security Advisories - MS-ISAC
Blog — PlanetScale
Blog — PlanetScale
Microsoft Security Blog
Microsoft Security Blog
D
Docker
WordPress大学
WordPress大学
罗磊的独立博客
J
Java Code Geeks
博客园 - 【当耐特】
博客园 - 司徒正美
雷峰网
雷峰网
H
Help Net Security
酷 壳 – CoolShell
酷 壳 – CoolShell
Last Week in AI
Last Week in AI
宝玉的分享
宝玉的分享
Martin Fowler
Martin Fowler
T
Tailwind CSS Blog
Google DeepMind News
Google DeepMind News
M
MIT News - Artificial intelligence
Recent Announcements
Recent Announcements
B
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
The DIR Net: A Distributed System for Detection, Isolatio...
Vincenzo De Florio · 2015-04-28 · via cs.DC updates on arXiv.org

This document describes the DIR net, a distributed environment which is part of the EFTOS fault tolerance framework. The DIR net is a system consisting of two components, called DIR Manager (or, shortly, the manager) and DIR Backup Agent (shortly, the backup). One manager and a set of backups is located in the system to be `guarded', one component per node. At this point the DIR net weaves a web which substantially does two things: 1) makes itself tolerant to a number of possible faults, and 2) gathers information pertaining the run of the user application. As soon as an error occurs within the DIR net, the system executes built-in recovery actions that allow itself to continue processing despite a number of hardware/software faults, possibly doing a graceful degradation of its features; when an error occurs in the user application, the DIR net, by means of custom- and user-defined detection tools, is informed of such events and runs one or more recovery strategies, both built-in and coded by the user using an ancillary compile-time tool, the rl translator. Such tools translates the user-defined strategies into a binary `R-code', i.e., a pseudo-code interpreted by a special component of the DIR net, the Recovery Interpreter, rint (in a sense, rint is a r-code virtual machine.) This document describes the generic component of the DIR net, a function which can behave either as manager or as backup.