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RFPL: A Recovery Friendly Parity Logging Scheme for Reducing Small Write Penalty of SSD RAID

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Published:05 August 2019Publication History

ABSTRACT

Parity based RAID suffers from poor small write performance due to heavy parity update overhead. The recently proposed method EPLOG constructs a new stripe with updated data chunks without updating old parity chunks. However, due to skewness of data accesses, old versions of updated data chunks often need to be kept to protect other data chunks of the same stripe. This seriously hurts the efficiency of recovering system from device failures due to the need of reconstructing the preserved old data chunks on failed devices.

In this paper, we propose a Recovery Friendly Parity Logging scheme, called RFPL, which minimizes small write penalty and provides high recovery performance for SSD RAID. The key idea of RFPL is to reduce the mixture of old and new data chunks in a stripe by exploiting skewness of data accesses. RFPL constructs a new stripe with updated data chunks of the same old stripe. Since cold data chunks of the old stripe are rarely updated, it is likely that all of data chunks written to the new stripe are hot data and become old together within a short time span. This co-old of data chunks in a stripe effectively mitigates the total number of old data chunks which need to be preserved. We have implemented RFPL on a RAID-5 SSD array in Linux 4.3. Experimental results show that, compared with the Linux software RAID, RFPL reduces user I/O response time by 83.1% for normal state and 81.6% for reconstruction state. Compared with the state-of-the-art scheme EPLOG, RFPL reduces user I/O response time by 46.8% for normal state and 40.9% for reconstruction state. Our reliability analysis shows RFPL improves the mean time to data loss (MTTDL) by 9.36X and 1.44X compared with the Linux software RAID and EPLOG.

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  • Published in

    cover image ACM Other conferences
    ICPP '19: Proceedings of the 48th International Conference on Parallel Processing
    August 2019
    1107 pages
    ISBN:9781450362955
    DOI:10.1145/3337821

    Copyright © 2019 ACM

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    New York, NY, United States

    Publication History

    • Published: 5 August 2019

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