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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
IORM: Hierarchical I/O Governance for Thousands of Consol...
Rajarshi Chowdhury, Akshay Shah, Zakaria Alrmaih, Chenhao Guo, A · 2026-05-28 · via cs.DB updates on arXiv.org

Oracle Exadata consolidates thousands of tenant databases onto shared storage infrastructure deployed at hundreds of customer sites worldwide. Oracle Multitenant architecture enables this extreme density, with thousands of tenant databases sharing a single Exadata storage system -- but this creates a multi-level resource hierarchy (container databases, tenant databases, and workloads within tenants) that commodity block-layer schedulers cannot govern, as they lack visibility into database semantics and tenant boundaries. This paper presents the I/O Resource Manager (IORM), a storage-side scheduler built on three mechanisms: I/O Tagging, which propagates semantic context from the database kernel to the storage scheduler; Hierarchical Resource Profiles, which express compositional allocation policies across consolidation tiers using shares and limits; and Unified Storage Governance, which applies these policies consistently across all tiers of the storage hierarchy -- persistent memory, flash, and hard disk -- including cache placement decisions. IORM enables successful cloud deployments where thousands of tenants coexist on shared storage: production OLTP workloads run alongside concurrent analytical workloads from the same or different databases without noisy-neighbor interference. Evaluation on production Exadata systems demonstrates that IORM dramatically improves latency consistency, virtually eliminating tail latency outliers and delivering several-fold improvements in average read latency under mixed workloads. Hierarchical limits compose correctly across all three levels, and proportional share allocation tracks configured ratios closely even under highly skewed demand.