Cheng Chen, Jun Yang, Q. Wei, Chundong Wang, Mingdi Xue
{"title":"在NVM上进行细粒度元数据日志记录","authors":"Cheng Chen, Jun Yang, Q. Wei, Chundong Wang, Mingdi Xue","doi":"10.1109/MSST.2016.7897077","DOIUrl":null,"url":null,"abstract":"Journaling file systems have been widely used where data consistency must be assured. However, we observed that the overhead of journaling can cause up to 48.2% performance drop under certain kinds of workloads. On the other hand, the emerging high-performance, byte-addressable Non-volatile Memory (NVM) has the potential to minimize such overhead by being used as the journal device. The traditional journaling mechanism based on block devices is nevertheless unsuitable for NVM due to the write amplification of metadata journal we observed. In this paper, we propose a fine-grained metadata journal mechanism to fully utilize the low-latency byte-addressable NVM so that the overhead of journaling can be significantly reduced. Based on the observation that conventional block-based metadata journal contains up to 90% clean metadata that is unnecessary to be journalled, we design a fine-grained journal format for byte-addressable NVM which contains only modified metadata. Moreover, we redesign the process of transaction committing, checkpointing and recovery in journaling file systems utilizing the new journal format. Therefore, thanks to the reduced amount of ordered writes to NVM, the overhead of journaling can be reduced without compromising the file system consistency. Experimental results show that our NVM-based fine-grained metadata journaling is up to 15.8× faster than the traditional approach under FileBench workloads.","PeriodicalId":299251,"journal":{"name":"2016 32nd Symposium on Mass Storage Systems and Technologies (MSST)","volume":"17 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2016-05-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"42","resultStr":"{\"title\":\"Fine-grained metadata journaling on NVM\",\"authors\":\"Cheng Chen, Jun Yang, Q. Wei, Chundong Wang, Mingdi Xue\",\"doi\":\"10.1109/MSST.2016.7897077\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"Journaling file systems have been widely used where data consistency must be assured. However, we observed that the overhead of journaling can cause up to 48.2% performance drop under certain kinds of workloads. On the other hand, the emerging high-performance, byte-addressable Non-volatile Memory (NVM) has the potential to minimize such overhead by being used as the journal device. The traditional journaling mechanism based on block devices is nevertheless unsuitable for NVM due to the write amplification of metadata journal we observed. In this paper, we propose a fine-grained metadata journal mechanism to fully utilize the low-latency byte-addressable NVM so that the overhead of journaling can be significantly reduced. Based on the observation that conventional block-based metadata journal contains up to 90% clean metadata that is unnecessary to be journalled, we design a fine-grained journal format for byte-addressable NVM which contains only modified metadata. Moreover, we redesign the process of transaction committing, checkpointing and recovery in journaling file systems utilizing the new journal format. Therefore, thanks to the reduced amount of ordered writes to NVM, the overhead of journaling can be reduced without compromising the file system consistency. Experimental results show that our NVM-based fine-grained metadata journaling is up to 15.8× faster than the traditional approach under FileBench workloads.\",\"PeriodicalId\":299251,\"journal\":{\"name\":\"2016 32nd Symposium on Mass Storage Systems and Technologies (MSST)\",\"volume\":\"17 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2016-05-02\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"42\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"2016 32nd Symposium on Mass Storage Systems and Technologies (MSST)\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1109/MSST.2016.7897077\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"2016 32nd Symposium on Mass Storage Systems and Technologies (MSST)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/MSST.2016.7897077","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
Journaling file systems have been widely used where data consistency must be assured. However, we observed that the overhead of journaling can cause up to 48.2% performance drop under certain kinds of workloads. On the other hand, the emerging high-performance, byte-addressable Non-volatile Memory (NVM) has the potential to minimize such overhead by being used as the journal device. The traditional journaling mechanism based on block devices is nevertheless unsuitable for NVM due to the write amplification of metadata journal we observed. In this paper, we propose a fine-grained metadata journal mechanism to fully utilize the low-latency byte-addressable NVM so that the overhead of journaling can be significantly reduced. Based on the observation that conventional block-based metadata journal contains up to 90% clean metadata that is unnecessary to be journalled, we design a fine-grained journal format for byte-addressable NVM which contains only modified metadata. Moreover, we redesign the process of transaction committing, checkpointing and recovery in journaling file systems utilizing the new journal format. Therefore, thanks to the reduced amount of ordered writes to NVM, the overhead of journaling can be reduced without compromising the file system consistency. Experimental results show that our NVM-based fine-grained metadata journaling is up to 15.8× faster than the traditional approach under FileBench workloads.