Data Delta Based Hybrid Writes for Erasure-Coded Storage Systems

Huang, Qiang; Chen, Hui; Wei, Bing; Wu, Jigang; Xiao, Limin

doi:10.1007/978-3-030-93571-9_14

Cited by 1 publication

(1 citation statement)

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“…In other words, "write-readafter-write" needs to be executed for the first update, and subsequent updates only require "write-after-write", which can reduce disk seek overhead to some extent. Based on the work of reference [2], reference [17] further combines erasure codes with replicas and schedules I/O at a finer granularity, transforming repetitive "read-afterwrite" into "read-after-write" + "write", further reducing I/O overhead. These methods are based on scenarios where the same data is repeatedly updated by applications.…”

Section: Related Workmentioning

confidence: 99%

Cognitive Update and Fast Recovery Using Machine Learning and Parity Delta Shareability for Erasure-Coded Storage Systems

Wei,

Shi,

et al. 2024

Preprint

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Erasure-coded systems require simultaneous updating of data chunks and parity chunks, thereby leading to high input/output (I/O) overhead. Following these updates, the most recent data and parity deltas are dispersed randomly in the log, which results in prolonged recovery time. This study introduces a novel approach named MPLR, which is specifically designed to expedite erasure-coded data updating and recovery. MPLR uses machine learning to categorize files into non-read-only and read-only classes, and then performs updates based on these classifications. For non-read-only files, MPLR applies in-place updates for data chunks and log-based updates for parity chunks, while setting aside disk space adjacent to the parity chunk to minimize disk seeks. For read-only files, MPLR likewise employs a mixture of in-place data updates and log-based parity updates, although without reserving space next to parity chunks. In regard to data recovery, MPLR enhances speed by accessing the parity delta stored in the allocated disk space. To further boost recovery acceleration, MPLR reads the parity delta in parallel and reduces the volume of parity delta leveraging the shareable aspects of the parity delta. Experimental results confirm the efficiency of MPLR in significantly improving update and recovery performances, compared to the state-of-the-art approaches.

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Section: Related Workmentioning

confidence: 99%