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

U
Unit 42
博客园 - 司徒正美
V
Visual Studio Blog
博客园 - 【当耐特】
T
Tailwind CSS Blog
美团技术团队
博客园 - 叶小钗
Jina AI
Jina AI
宝玉的分享
宝玉的分享
IT之家
IT之家
Hugging Face - Blog
Hugging Face - Blog
雷峰网
雷峰网
Stack Overflow Blog
Stack Overflow Blog
博客园_首页
人人都是产品经理
人人都是产品经理
T
The Blog of Author Tim Ferriss
P
Proofpoint News Feed
Microsoft Security Blog
Microsoft Security Blog
Y
Y Combinator Blog
GbyAI
GbyAI
大猫的无限游戏
大猫的无限游戏
Martin Fowler
Martin Fowler
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
腾讯CDC

cs.DB updates on arXiv.org

Block-Sphere Vector Quantization GroupAffect-4: A Multimodal Dataset of Four-Person Collaborative Interaction CogScale: Scalable Benchmark for Sequence Processing TextAlign: Preference Alignment for Text Rendering with Hierarchical Rewards LogRouter: Adaptive Two-Level LLM Routing for Log Question Answering in Big Data Systems Agentic Cost-Aware Query Planning with Knowledge Distillation for Big Data Analytics Covariance Structure and Coordinate Heterogeneity Govern Binary Quantization of Contrastive Embeddings IVF-TQ: Calibration-Free Streaming Vector Search via a Codebook-Free Residual Layer Automatic Unsupervised Ensemble Outlier Model Selection--Extended Version A Generative AI Framework for Intelligent Utility Billing CO 2 Analytics and Sustainable Resource Optimisation Towards Foundation Models for Relational Databases with Language Models and Graph Neural Networks Gaussian Relational Graph Transformer Croissant Baker: Metadata Generation for Discoverable, Governable, and Reusable ML Datasets Reducing Hallucination in Vision-Language Models via Stage-wise Preference Optimization under Distribution Shift A Horn extension of DL-Lite with NL data complexity 3D Primitives are a Spatial Language for VLMs Enabling AI-Native Mobility in 6G: A Real-World Dataset for Handover, Beam Management, and Timing Advance A CAP-like Trilemma for Large Language Models: Correctness, Non-bias, and Utility under Semantic Underdetermination EpiCastBench: Datasets and Benchmarks for Multivariate Epidemic Forecasting FERMI: Exploiting Relations for Membership Inference Against Tabular Diffusion Models Toward Multi-Database Query Reasoning for Text2Cypher Autonomous FAIR Digital Objects: From Passive Assertions to Active Knowledge HOME-KGQA: A Benchmark Dataset for Multimodal Knowledge Graph Question Answering on Household Daily Activities Detect, Localize, and Explain: Interactive Hierarchical Log Anomaly Analytics with LLM Augmentation Open Ontologies: Tool-Augmented Ontology Engineering with Stable Matching Alignment Machine Learning-Based Pre-Test Risk Stratification for PCR-Confirmed Chlamydia Using Patient-Reported Data and Urine Biomarkers Reconciling Consistency-Based Diagnosis with Actual-Causality-Based Explanations PrepBench: How Far Are We from Natural-Language-Driven Data Preparation? Anatomy of a Query: W5H Dimensions and FAR Patterns for Text-to-SQL Evaluation Building informative materials datasets beyond targeted objectives
SeeMoRe: A Fault-Tolerant Protocol for Hybrid Cloud Envir...
Mohammad Javad Amiri, Sujaya Maiyya, Divyakant Agrawal, Amr El A · 2019-06-19 · via cs.DB updates on arXiv.org

Large scale data management systems utilize State Machine Replication to provide fault tolerance and to enhance performance. Fault-tolerant protocols are extensively used in the distributed database infrastructure of large enterprises such as Google, Amazon, and Facebook, as well as permissioned blockchain systems like IBM's Hyperledger Fabric. However, and in spite of years of intensive research, existing fault-tolerant protocols do not adequately address all the characteristics of distributed system applications. In particular, hybrid cloud environments consisting of private and public clouds are widely used by enterprises. However, fault-tolerant protocols have not been adapted for such environments. In this paper, we introduce SeeMoRe, a hybrid State Machine Replication protocol to handle both crash and malicious failures in a public/private cloud environment. SeeMoRe considers a private cloud consisting of nonmalicious nodes (either correct or crash) and a public cloud with both Byzantine faulty and correct nodes. SeeMoRe has three different modes which can be used depending on the private cloud load and the communication latency between the public and the private cloud. We also introduce a dynamic mode switching technique to transition from one mode to another. Furthermore, we evaluate SeeMoRe using a series of benchmarks. The experiments reveal that SeeMoRe's performance is close to the state of the art crash fault-tolerant protocols while tolerating malicious failures.