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

S
SegmentFault 最新的问题
爱范儿
爱范儿
博客园 - Franky
OSCHINA 社区最新新闻
OSCHINA 社区最新新闻
WordPress大学
WordPress大学
宝玉的分享
宝玉的分享
雷峰网
雷峰网
酷 壳 – CoolShell
酷 壳 – CoolShell
IT之家
IT之家
有赞技术团队
有赞技术团队
美团技术团队
Last Week in AI
Last Week in AI
奇客Solidot–传递最新科技情报
奇客Solidot–传递最新科技情报
大猫的无限游戏
大猫的无限游戏
The Cloudflare Blog
Jina AI
Jina AI
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
Engineering at Meta
Engineering at Meta
T
Tailwind CSS Blog
J
Java Code Geeks
Martin Fowler
Martin Fowler
I
InfoQ
小众软件
小众软件
MongoDB | Blog
MongoDB | 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
An Improved Multiple Faults Reassignment based Recovery i...
Sanjay Bansal, Sanjeev Sharma · 2011-02-14 · via cs.DC updates on arXiv.org

In case of multiple node failures performance becomes very low as compare to single node failure. Failures of nodes in cluster computing can be tolerated by multiple fault tolerant computing. Existing recovery schemes are efficient for single fault but not with multiple faults. Recovery scheme proposed in this paper having two phases; sequentially phase, concurrent phase. In sequentially phase, loads of all working nodes are uniformly and evenly distributed by proposed dynamic rank based and load distribution algorithm. In concurrent phase, loads of all failure nodes as well as new job arrival are assigned equally to all available nodes by just finding the least loaded node among the several nodes by failure nodes job allocation algorithm. Sequential and concurrent executions of algorithms improve the performance as well better resource utilization. Dynamic rank based algorithm for load redistribution works as a sequential restoration algorithm and reassignment algorithm for distribution of failure nodes to least loaded computing nodes works as a concurrent recovery reassignment algorithm. Since load is evenly and uniformly distributed among all available working nodes with less number of iterations, low iterative time and communication overheads hence performance is improved. Dynamic ranking algorithm is low overhead, high convergence algorithm for reassignment of tasks uniformly among all available nodes. Reassignments of failure nodes are done by a low overhead efficient failure job allocation algorithm. Test results to show effectiveness of the proposed scheme are presented.