hStorage-DB：异构感知数据管理，充分利用混合存储系统的能力
hStorage-DB: Heterogeneity-aware Data Management to Exploit the Full Capability of Hybrid Storage Systems
摘要：
随着存储系统变得越来越异构和复杂，它增加了DBA的负担，即使在进行了大量人工工作之后，也会导致性能不佳。此外，现有的通过访问模式检测的基于监视的存储管理难以处理高度动态和并发的工作负载。为了最好地利用异构存储设备实现高性能，我们设计并实现了一个异构感知的DBMS存储管理的软件框架称为异构DB，语义信息是关键的存储I/O被识别和传递到存储管理器。根据收集的语义信息，请求被分为不同的类型。每种类型都分配有底层存储系统支持的适当QoS策略，以便每个请求都将由合适的存储设备提供服务。使用hash-DB，我们可以很好地利用语义信息，这些信息不能通过数据访问监控来检测，但对于混合存储系统特别重要。为了显示hash-DB的有效性，我们已经实现了一个系统原型，该原型由一个I/O请求分类启用DBMS，和一个混合存储系统，组织成一个两级缓存层次结构。我们的性能评估表明，hundre-DB可以自动作出适当的决定，在不同的存储设备的数据分配，并作出实质性的性能改善，在一个成本效益的方式。
英文摘要：
As storage systems become increasingly heterogeneous and complex, it adds burdens on DBAs, causing suboptimal performance even after a lot of human efforts have been made. In addition, existing monitoring-based storage management by access pattern detections has difficulties to handle workloads that are highly dynamic and concurrent. To achieve high performance by best utilizing heterogeneous storage devices, we have designed and implemented a heterogeneity-aware software framework for DBMS storage management called hStorage-DB, where semantic information that is critical for storage I/O is identified and passed to the storage manager. According to the collected semantic information, requests are classified into different types. Each type is assigned a proper QoS policy supported by the underlying storage system, so that every request will be served with a suitable storage device. With hStorage-DB, we can well utilize semantic information that cannot be detected through data access monitoring but is particularly important for a hybrid storage system. To show the effectiveness of hStorage-DB, we have implemented a system prototype that consists of an I/O request classification enabled DBMS, and a hybrid storage system that is organized into a two-level caching hierarchy. Our performance evaluation shows that hStorage-DB can automatically make proper decisions for data allocation in different storage devices and make substantial performance improvements in a cost-efficient way.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
