Performance Insulation and Predictability for Shared Cluster Storage

本研究探讨设计和实施策略，以隔离高端计算应用程序共享集群存储系统的性能。 特别是，这种共享不应导致意外的低效率。虽然每个应用程序可能会看到较低的性能，但由于只获得I/O系统总关注的一小部分，因此任何应用程序都不应该看到比它收到的那部分更少的工作。 理想情况下，不应该由于应用程序之间的干扰而浪费I/O资源，并且一组应用程序实现的I/O性能应该是其非共享性能的可预测部分。 不幸的是，大多数存储系统都不是这样，这使得管理复杂化，并对共享存储基础设施的存储系统造成不利影响。要实现所需的隔离和可预测性，需要明确避免干扰的缓存管理、磁盘布局、磁盘调度和存储节点选择策略。 这项研究结合并建立在数据库系统的技术之上（例如，访问模式整形和查询专用高速缓存管理）和存储/文件系统（例如，磁盘调度和存储节点选择）。 两种具体技术是：（1）使用知道与数据和请求相关联的应用的预取和回写，可以避免降低效率的交错;（2）基于通过识别每个工作负载的访问模式确定的每个工作负载的益处来划分该高速缓存空间，一个应用的数据不能在存储服务器高速缓存中获得无限的占用空间。

This research explores design and implementation strategies for insulating the performance of high-end computing applications sharing a cluster storage system. In particular, such sharing should not cause unexpected inefficiency. While each application may see lower performance, due to only getting a fraction of the total attention of the I/O system, none should see less work accomplished than the fraction it receives. Ideally, no I/O resources should be wasted due to interference between applications, and the I/O performance achieved by a set of applications should be predictable fractions of their non-sharing performance. Unfortunately, neither is true of most storage systems, complicating administration and penalizing those that share storage infrastructures.Accomplishing the desired insulation and predictability requires cache management, disk layout, disk scheduling, and storage-node selection policies that explicitly avoid interference. This research combines and builds on techniques from database systems (e.g., access pattern shaping and query-specific cache management) and storage/file systems (e.g., disk scheduling and storage-node selection). Two specific techniques are: (1) Using prefetching and write-back that is aware of the applications associated with data and requests, efficiency-reducing interleaving can be avoided; (2) Partitioning the cache space based on per-workload benefits, determined by recognizing each workload's access pattern, one application's data cannot get an unbounded footprint in the storage server cache.