Improving Flash Translation Layer Performance by Using Log Block Mapping Scheme and Two-level Buffer for Address Translation Information

Yinxia Xu
{"title":"Improving Flash Translation Layer Performance by Using Log Block Mapping Scheme and Two-level Buffer for Address Translation Information","authors":"Yinxia Xu","doi":"10.1145/3366715.3366746","DOIUrl":null,"url":null,"abstract":"In the era of big data, the requirement of mass storage and fast access of data makes solid state disk(SSD) based on NAND flash be widely used. However, increasing flash memory capacity imposes huge SRAM consumption for logical-physical translation table in a page-level flash translation layer(FTL). Existing FTL schemes selectively cache the on-demand address mappings to quicken the address translation, while keeping all address mappings in flash memory. But the page-level catching mechanism causes a certain degree of cache pollution. In this paper, we manage page-level address translation information at hybrid-level mapping scheme and use two-level buffer for map groups to decrease SRAM consumption while reducing the cache pollution. What's more, an efficient replacement policy is designed. We can increase the cache hit ratio and reduce the write backs of evicted dirty entries and decrease garbage collection operations by these means. The performance and lifetime of the flash memory is improved. Experimental results show that the proposed scheme increases cache hit ratio by up to 28% and decreases the average response time by up to 23% compared with the existing FTL schemes.","PeriodicalId":425980,"journal":{"name":"Proceedings of the 2019 International Conference on Robotics Systems and Vehicle Technology - RSVT '19","volume":"83 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2019-10-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"1","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Proceedings of the 2019 International Conference on Robotics Systems and Vehicle Technology - RSVT '19","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1145/3366715.3366746","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 1

Abstract

In the era of big data, the requirement of mass storage and fast access of data makes solid state disk(SSD) based on NAND flash be widely used. However, increasing flash memory capacity imposes huge SRAM consumption for logical-physical translation table in a page-level flash translation layer(FTL). Existing FTL schemes selectively cache the on-demand address mappings to quicken the address translation, while keeping all address mappings in flash memory. But the page-level catching mechanism causes a certain degree of cache pollution. In this paper, we manage page-level address translation information at hybrid-level mapping scheme and use two-level buffer for map groups to decrease SRAM consumption while reducing the cache pollution. What's more, an efficient replacement policy is designed. We can increase the cache hit ratio and reduce the write backs of evicted dirty entries and decrease garbage collection operations by these means. The performance and lifetime of the flash memory is improved. Experimental results show that the proposed scheme increases cache hit ratio by up to 28% and decreases the average response time by up to 23% compared with the existing FTL schemes.
查看原文
分享 分享
微信好友 朋友圈 QQ好友 复制链接
本刊更多论文
利用日志块映射和两级地址转换信息缓冲区提高Flash转换层性能
在大数据时代,海量存储和快速访问数据的需求使得基于NAND闪存的固态硬盘(SSD)得到了广泛的应用。然而,不断增加的闪存容量会对页级闪存转换层(FTL)中的逻辑物理转换表造成巨大的SRAM消耗。现有的FTL方案选择性地缓存按需地址映射以加快地址转换,同时将所有地址映射保存在闪存中。但是页级捕获机制会造成一定程度的缓存污染。在本文中,我们在混合级映射方案中管理页级地址转换信息,并对映射组使用两级缓冲区,以减少SRAM的消耗,同时减少缓存污染。并设计了有效的替代政策。通过这些方法,我们可以提高缓存命中率,减少被驱逐的脏条目的回写,减少垃圾收集操作。提高了闪存的性能和寿命。实验结果表明,与现有的FTL方案相比,该方案的缓存命中率提高了28%,平均响应时间降低了23%。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
求助全文
约1分钟内获得全文 去求助
来源期刊
自引率
0.00%
发文量
0
期刊最新文献
An Integrated Classification Model for Massive Short Texts with Few Words Stock Index Prediction Method Based on Dynamic Weighted Ensemble Learning An Improved A* Path Planning Algorithm for Indoor Intelligent Robot Research on Energy-Regeneration Simulation of An Electromechanical Active-Suspension System Research of PM2.5 Real-Time Prediction Model in Spark Cluster Environment
×
引用
GB/T 7714-2015
复制
MLA
复制
APA
复制
导出至
BibTeX EndNote RefMan NoteFirst NoteExpress
×
×
提示
您的信息不完整,为了账户安全,请先补充。
现在去补充
×
提示
您因"违规操作"
具体请查看互助需知
我知道了
×
提示
现在去查看 取消
×
提示
确定
0
微信
客服QQ
Book学术公众号 扫码关注我们
反馈
×
意见反馈
请填写您的意见或建议
请填写您的手机或邮箱
已复制链接
已复制链接
快去分享给好友吧!
我知道了
×
扫码分享
扫码分享
Book学术官方微信
Book学术文献互助
Book学术文献互助群
群 号:604180095
Book学术
文献互助 智能选刊 最新文献 互助须知 联系我们:info@booksci.cn
Book学术提供免费学术资源搜索服务,方便国内外学者检索中英文文献。致力于提供最便捷和优质的服务体验。
Copyright © 2023 Book学术 All rights reserved.
ghs 京公网安备 11010802042870号 京ICP备2023020795号-1