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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
Towards Efficient Flash Caches with Emerging NVMe Flexibl...
Michael Allison, Arun George, Javier Gonzalez, Dan Helmick, Vika · 2025-02-21 · via cs.DB updates on arXiv.org

NVMe Flash-based SSDs are widely deployed in data centers to cache working sets of large-scale web services. As data centers face increasing sustainability demands, such as reduced carbon emissions, efficient management of Flash overprovisioning and endurance has become crucial. Our analysis demonstrates that mixing data with different lifetimes on Flash blocks results in high device garbage collection costs, which either reduce device lifetime or necessitate host overprovisioning. Targeted data placement on Flash to minimize data intermixing and thus device write amplification shows promise for addressing this issue. The NVMe Flexible Data Placement (FDP) proposal is a newly ratified technical proposal aimed at addressing data placement needs while reducing the software engineering costs associated with past storage interfaces, such as ZNS and Open-Channel SSDs. In this study, we explore the feasibility, benefits, and limitations of leveraging NVMe FDP primitives for data placement on Flash media in CacheLib, a popular open-source Flash cache widely deployed and used in Meta's software ecosystem as a caching building block. We demonstrate that targeted data placement in CacheLib using NVMe FDP SSDs helps reduce device write amplification, embodied carbon emissions, and power consumption with almost no overhead to other metrics. Using multiple production traces and their configurations from Meta and Twitter, we show that an ideal device write amplification of ~1 can be achieved with FDP, leading to improved SSD utilization and sustainable Flash cache deployments.