Solid-state drive control device and learning-based solid-state drive data access method
The invention discloses a solid-state drive control device and a learning-based solid-state drive data access method, wherein the method comprises the steps of: presetting a hash table, the hash table comprising more than one hash value, the hash value is used to record and represent data characteristics of data pages in the solid-state drive. Obtaining an I/O data stream of the solid-state drive, and obtaining a hash value corresponding to the I/O data stream in the hash table. Predicting a sequence of data pages and/or data pages that are about to be accessed by a preset first learning model. Prefetching data is performed in the solid-state drive based on an output result of the first learning model. Through the embodiment of the present invention, when predicting prefetched data, learning can be performed in real time to adapt to different application categories and access modes through adaptive adjustment parameters, so that better data prefetching performance can be obtained.
1. A method for accessing data of a solid-state drive, comprising:
presetting a hash table that comprises one or more hash values, wherein each of the hash values represents a plurality of data characteristics of a corresponding data page of the solid-state drive, the data characteristics comprising an access frequency of the corresponding data page and a re-reference interval of the corresponding data page, wherein a file is written into a plurality of data pages of the solid-state drive including the corresponding data page;
obtaining an I/O data stream of the solid-state drive;
identifying at least one of the hash values in the hash table based on the obtained I/O data stream;
predicting information related to a data page to be accessed or a data page sequence to be accessed using a learning model, wherein an input of the learning model includes a set of the plurality of data characteristics represented by the identified at least one hash value, wherein the learning model includes a reinforcement learning model, a prefetch performance indicator for the data of the solid-state drive is set, and the prefetch performance indicator indicates a prefetch hit rate,
wherein when an output result of the learning model is less than the prefetch performance indicator or the prefetch performance indicator decreases, a penalty value is fed back as a second input of the learning model to adjust a set of weights in the learning model, and
wherein when the output result of the learning model causes the prefetch performance indicator to increase, a reward value is fed back as the second input of the learning model to stimulate and increase one or more weights associated with one or more valid data characteristics in the plurality of data characteristics; and
determining I/O data to be accessed from the solid-state drive based on the predicted information.
2. The method of claim 1 , further comprising fetching the determined I/O data into a cache.
3. The method of claim 1 , wherein the information related to the data page to be accessed or the data page sequence to be accessed comprises at least one of a plurality of data pages of the solid-state drive to be accessed or a sequence of the plurality of data pages.
4. The method of claim 1 , wherein the learning model further includes deep learning.
5. The method of claim 4 , wherein the learning model comprises a plurality of neural structure layers comprising an output layer, one or more coding layers, and one or more decoding layers,
wherein the one or more coding layers and decoding layers are symmetric to one another with respect to the output layer; and
a weight matrix of each of the coding layers is transposed as a weight matrix of a respective decoding layer that is symmetric to the coding layer.
6. The method of claim 1 , wherein the plurality of data characteristics further comprise a file name, a file type, and a file size of the file to which the corresponding data page belongs.
7. The method of claim 6 , wherein the plurality of data characteristics further comprise access history data of the corresponding data page and a data category of the corresponding data page.