如何提高基于深度挖掘的PCM存储设备的空间利用率?

Chun-Ta Lin, Yuan-Hao Chang, Tei-Wei Kuo, Hung-Sheng Chang, Hsiang-Pang Li
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引用次数: 6

摘要

近年来,特别是随着硬件计算能力的迅速提高,人们对存储设备的智能化要求越来越高。本文研究了基于深度挖掘的非易失性相变存储器(PCM)空间利用的基本设计问题。我们探索采用数据复制技术来减少PCM存储设备上潜在的数据重复,从而提供比物理存储空间更多的存储空间。在各种重复数据删除技术中,可变大小的分块被认为是成本较低的基于pcm的存储设备,因为可变大小的分块比固定大小的分块具有更好的重复数据删除能力。然而,在典型的系统架构中,数据是在固定的管理单元(例如,lba)中写入或更新的。因此,为了最终提高基于PCM的存储设备的空间利用率,技术问题在于(1)如何将固定大小的lba映射到可变大小的块,(2)如何有效地管理(即分配和释放)可变大小块的空闲PCM存储空间。为了解决以上两个问题,本文提出了一种基于容器的空间管理器,即基于容器的空间管理器,利用存储系统在初始阶段拥有更多的自由空间来缓解空间管理的复杂性,以及当存储系统的数据量越来越大时,存储系统的空间优化可以随着时间的推移而增长。建议的设计在流行的基准上进行了评估,我们得到了非常令人鼓舞的结果。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
How to improve the space utilization of dedup-based PCM storage devices?
There is a growing demand to introduce more and more intelligence to storage devices in recent years, especially with the rapid increasing of hardware computing power. This paper targets on essential design issues in space utilization for dedup-based non-volatile phase-change memory (PCM). We explore the adoption of data duplication techniques to reduce potential data duplicates over PCM storage devices to provide more storage space than the physical storage space does. Among various data deduplication techniques, variable-sized chunking is considered in less cost-effective PCM-based storage devices because variable-sized chunking has better data deduplication capability than fixed-sized chunking. However, in a typical system architecture, data are written or updated in the fixed management units (e.g., LBAs). Thus, to ultimately improve the space utilization of PCM-based storage device, the technical problem falls on (1) how to map fixed-sized LBAs to variable-sized chunks and (2) how to efficiently manage (i.e., allocated and deallocate) free PCM storage space for variable-sized chunks. In this work, we propose a free space manager, called container-based space manager, to resolve the above two issues by exploiting the fact that (1) a storage system initially has more free space to relax the complexity on space management and (2) the space optimization of a storage system can grow with the time when it contains more and more data. The proposed design is evaluated over popular benchmarks, for which we have very encouraging results.
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