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

Abstract

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.