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.

翻译：Oracle Exadata将数千个租户数据库整合至部署在全球数百个客户站点的共享存储基础设施上。Oracle多租户架构实现了这种极高密度部署——数千个租户数据库共享同一套Exadata存储系统，但这也产生了多层资源层级结构（容器数据库、租户数据库及租户内工作负载），而通用块层调度器因缺乏对数据库语义和租户边界的感知能力，无法对其进行有效治理。本文提出I/O资源管理器（IORM），这是一种基于三种机制构建的存储端调度器：I/O标记，将语义上下文从数据库内核传播至存储调度器；分层资源配置文件，通过份额和限制来表达跨整合层的组合式分配策略；以及统一存储治理，将上述策略一致应用于存储层级的所有层级——持久内存、闪存和硬盘——包括缓存放置决策。IORM使得数千个租户在共享存储上共存的云部署成为可能：生产环境中的OLTP工作负载可与来自相同或不同数据库的并发分析型工作负载同时运行，且无噪声邻居干扰。在实际Exadata系统上的评估表明，IORM显著提升了延迟一致性，几乎消除了尾部延迟异常值，并在混合工作负载下将平均读取延迟提升了数倍。分层限制正确地在所有三个层级生效，即使在高度偏斜的需求下，比例份额分配也紧密遵循配置比例。