Towards Physical Design Management in Storage Systems

K. Dahlgren, J. LeFevre, Ashay Shirwadkar, Ken Iizawa, Aldrin Montana, P. Alvaro, C. Maltzahn
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引用次数: 3

Abstract

In the post-Moore era, systems and devices with new architectures will arrive at a rapid rate with significant impacts on the software stack. Applications will not be able to fully benefit from new architectures unless they can delegate adapting to new devices in lower layers of the stack. In this paper we introduce physical design management which deals with the problem of identifying and executing transformations on physical designs of stored data, i.e. how data is mapped to storage abstractions like files, objects, or blocks, in order to improve performance. Physical design is traditionally placed with applications, access libraries, and databases, using hard-wired assumptions about underlying storage systems. Yet, storage systems increasingly not only contain multiple kinds of storage devices with vastly different performance profiles but also move data among those storage devices, thereby changing the benefit of a particular physical design. We advocate placing physical design management in storage, identify interesting research challenges, provide a brief description of a prototype implementation in Ceph, and discuss the results of initial experiments at scale that are replicable using Cloudlab. These experiments show performance and resource utilization trade-offs associated with choosing different physical designs and choosing to transform between physical designs.
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面向存储系统的物理设计管理
在后摩尔时代,具有新架构的系统和设备将以快速的速度出现,对软件堆栈产生重大影响。应用程序将无法从新的架构中充分受益,除非它们能够委派适应堆栈较低层的新设备。在本文中,我们介绍了物理设计管理,它处理识别和执行存储数据的物理设计转换的问题,即数据如何映射到存储抽象,如文件、对象或块,以提高性能。传统上,物理设计是与应用程序、访问库和数据库一起进行的,使用关于底层存储系统的硬连线假设。然而,存储系统不仅包含多种具有不同性能配置文件的存储设备,而且还在这些存储设备之间移动数据,从而改变了特定物理设计的好处。我们提倡将物理设计管理放在存储中,确定有趣的研究挑战,在Ceph中提供原型实现的简要描述,并讨论可使用Cloudlab复制的大规模初始实验结果。这些实验表明,在选择不同的物理设计和选择在物理设计之间转换时,性能和资源利用的权衡是相关的。
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[Copyright notice] Applying Machine Learning to Understand Write Performance of Large-scale Parallel Filesystems Towards Physical Design Management in Storage Systems Profiling Platform Storage Using IO500 and Mistral Active Learning-based Automatic Tuning and Prediction of Parallel I/O Performance
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