Method and device for predicting burst access behavior of storage system
A technology of storage system and prediction method, which is applied in the field of prediction of sudden access behavior, to achieve the effects of high efficiency, stable frequent association mode, and excellent mining efficiency
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Embodiment 1
[0049] Such as figure 1 As shown, the storage system-oriented prediction device for burst access behavior in this embodiment includes an association mining module, an association filtering module, and a burst prediction module, wherein:
[0050] The associated mining module is used to take a part of the I / O (Input / Output, input / output) data set of the storage system as a training set, and carry out frequent associated I / O mining to the training set;
[0051] The association filtering module is used to divide the test set of the I / O data set of the storage system with the specified forecast time as the time granularity, and obtain the total number of I / O requests at the corresponding forecast time point of the test set; then extract each Predict the I / O request data in the specified observation time at the time point, filter the I / O request data at each observation time point, filter the frequently associated I / O at each observation time point, and keep each observation time P...
Embodiment 2
[0073] This embodiment is a specific application example of the method for predicting burst access behavior of a storage system in Embodiment 1.
[0074] to combine Figure 3a~3c Workflow diagram of the associated hardening window matching and Figure 4 A detailed analysis of the workflow flow chart of sudden access behavior prediction.
[0075] In order to make the Apriori algorithm more advantageous in mining such data sets used in this embodiment, a kind of Apriori algorithm based on the correlation strengthening window is proposed. The overall idea of the algorithm follows the idea of the Apriori algorithm, but in the processing In the third step, when scanning the database to match the candidate set with the entire data set, counting the support of the candidate set and deleting the candidate sets that do not meet the requirements, a correlation strengthening window is introduced, which is used to limit the data block. The amount of data, because each data block cor...
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