异构存储中硬件透明的I/O治理
Hardware-Transparent I/O Governance in Disaggregated Heterogeneous Storage
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中文总结 AI 辅助
异构存储集群面临性能和I/O限制治理问题，本文提出IORM调度器，结合硬件感知成本建模、量子速率限制和分布式反馈控制三种机制，解决上述问题，分享生产经验，测试集群验证其在极端偏斜、无干扰及故障恢复方面的良好性能。
中文摘要 AI 辅助
无共享的异构存储集群同时服务对延迟敏感的数据库和不透明的块卷工作负载，现有调度器无法解决两个治理问题：跨异构硬件代维持一致性能，以及在访问模式偏向部分存储节点时执行全局I/O限制。我们提出I/O资源管理器（IORM），一种在Oracle Exadata Exascale中生产部署的多级分布式调度器。IORM结合三种机制：硬件感知成本建模器、基于量子的速率限制器和分布式自适应反馈控制器。我们还分享生产部署的操作经验。在8节点测试集群上，IORM在极端顺序偏斜下收敛到配置限制的5%以内，无租户间干扰，存储节点故障15秒内恢复全吞吐量。
英文摘要
Shared-nothing disaggregated storage clusters that serve both latency-sensitive databases and opaque block-volume workloads face two governance problems unsolved by existing schedulers: maintaining consistent performance across heterogeneous hardware generations, and enforcing global I/O limits when access patterns skew to a subset of storage nodes. We present the I/O Resource Manager (IORM), a multi-stage distributed scheduler deployed in production within Oracle Exadata Exascale. IORM combines three mechanisms: a hardware-aware cost modeler that normalizes I/O accounting using datasheet-derived fixed costs to make limits invariant across hardware generations; a quantum-based rate limiter with bounded carry-forward credits that accommodates database micro-bursts while enforcing long-term SLOs; and a distributed adaptive feedback controller that redistributes unused entitlements across the cluster to resolve topological access skew. Beyond design, we share operational lessons from production deployment. On an 8-node test cluster running up to 100 concurrent tenant volumes, IORM converges within 5\% of provisioned limits under extreme sequential skew, scales without inter-tenant interference, and recovers full throughput within 15 seconds of a storage-node failure.
