Distributed storage data recovery scheduling method, system and device and storage medium

Abstract

The invention discloses a distributed storage data recovery scheduling method, system and device and a storage medium. The method comprises the following steps: selecting a check block and a data block to construct an inversion matrix as a reference matrix, labeling rows of the reference matrix according to a sequence, and recording an address; calculating a reference matrix inverse matrix, and labeling the columns of the reference matrix inverse matrix in sequence; inputting a to-be-scheduled inversion matrix, matching the to-be-scheduled inversion matrix with the elements of the reference matrix, defining the same label for the same row of the elements, and recording a new address; and correspondingly scheduling column elements of the inverse matrix of the reference matrix according to the relative change of the row address of the reference matrix and the row address of the to-be-scheduled inverse matrix with the same label to obtain an inverse matrix after the input to-be-scheduledinverse matrix is scheduled, wherein the inverse matrix is used for distributed storage data recovery. Under the same error condition, simple reference matrix element scheduling can be carried out based on any reference matrix, and the decoding speed and efficiency of the erasure circuit under different disk reading sequences are improved.

Description

technical field [0001] The data storage field of the present invention specifically relates to a distributed storage data recovery scheduling method, system, equipment and storage medium. Background technique [0002] Faced with the storage requirements of massive data, distributed storage has gradually replaced the dominant position of unified storage due to its advantages of low cost and good scalability, and has received more and more attention in theoretical research and practical applications. However, the reliability of distributed storage is insufficient. On the one hand, distributed storage systems mostly use cheap disks as storage nodes, and the reliability of each storage node is often not high. On the other hand, a distributed storage system usually includes many Nodes, due to software and hardware failures, human errors and other reasons, the system often has node failures. In order to improve the data reliability of the distributed storage system and ensure tha...

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Problems solved by technology

But the disadvantage is that it is not optimized for the characteristics of matrix coding, so although the calculation is regularized, it will introduce a lot of redundant operations

Especially when recovering data in a distributed storage scenario, for the same error code block, although the row and column elements are the same using the Gaussian elimination method, as long as the order in which the hardware reads data changes, the inverse matrix needs to be recalculated, which will cause huge resource consumption

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