YouChoose:为整合存储系统提供方便高效的QoS支持的性能接口

Xuechen Zhang, Yuehai Xu, Song Jiang
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引用次数: 11

摘要

目前,基于磁盘的存储系统的QoS要求通常以服务水平协议(SLA)的形式表示,以绑定I/O度量,如I/O请求的延迟和吞吐量。然而,出于两个主要原因,SLA并不是用户指定所需I/O服务质量的有效性能接口。首先,对于用户来说,很难确定适当的延迟和吞吐量界限,以确保他们的应用程序性能,而不会出现资源过度供应。其次,对于存储系统管理员来说,估计用户的实际资源需求是一个挑战,因为指定的SLA度量与用户的资源需求并不一致。这使得资源配置和调度信息较少,并可能大大降低系统效率。我们提出了参考存储系统(RSS)的概念,它可以是用户选择的存储系统,其性能可以离线测量和在线模拟,作为应用程序和存储服务器之间的性能接口。通过指定一个RSS来表示I/O性能需求,用户可以期望从为其I/O工作负载提供服务的共享存储服务器收到的性能不会比为相同工作负载提供服务的RSS收到的性能差。存储系统负责实现RSS接口。关键的启用技术是派生特定于请求的性能需求的机器学习模型,以及在来自不同用户的请求之间有效地分配资源的以rss为中心的调度。该方案通过在主机存储系统中高效地实现和迁移虚拟存储设备,支持用户选择的性能接口。我们基于跟踪驱动仿真的评估表明,YouChoose可以精确地实现RSS性能接口,实现强大的性能保证和隔离,并提高由不同类型存储设备组成的统一存储系统的效率。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
YouChoose: A performance interface enabling convenient and efficient QoS support for consolidated storage systems
Currently the QoS requirements for disk-based storage systems are usually presented in the form of service-level agreement (SLA) to bound I/O measures such as latency and throughput of I/O requests. However, SLA is not an effective performance interface for users to specify their required I/O service quality for two major reasons. First, for users, it is difficult to determine appropriate latency and throughput bounds to ensure their application performance without resource over-provisioning. Second, for storage system administrators, it is a challenge to estimate a user's real resource demand because the specified SLA measures are not consistently correlated with the user's resource demand. This makes resource provisioning and scheduling less informative and could greatly reduce system efficiency. We propose the concept of reference storage system (RSS), which can be a storage system chosen by users and whose performance can be measured off-line and mimicked on-line, as a performance interface between applications and storage servers. By designating an RSS to represent I/O performance requirement, a user can expect the performance received from a shared storage server servicing his I/O workload is not worse than the performance received from the RSS servicing the same workload. The storage system is responsible for implementing the RSS interface. The key enabling techniques are a machine learning model that derives request-specific performance requirements and an RSS-centric scheduling that efficiently allocates resource among requests from different users. The proposed scheme, named as YouChoose, supports the user-chosen performance interface through efficiently implementing and migrating virtual storage devices in a host storage system. Our evaluation based on trace-driven simulations shows that YouChoose can precisely implement the RSS performance interface, achieve a strong performance assurance and isolation, and improve the efficiency of a consolidated storage system consisting of different types of storage devices.
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