A data scheduling method and system
A data scheduling and data technology, applied in transmission systems, electrical components, etc., can solve problems such as server load imbalance, inability to guarantee scheduling, and inability to provide guarantee for data scheduling and timely processing, and achieve the effect of improving accuracy and timeliness
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Embodiment 1
[0023] This embodiment provides a data scheduling method, such as figure 1 As shown, the method includes:
[0024] Step 101: Based on the historical processing performance data of at least one server, determine at least one attribute included in the historical processing performance data, and at least one category corresponding to each attribute;
[0025] Step 102: Establish a server evaluation model based on the historical processing performance data of the at least one server; wherein, the server evaluation model includes: at least one branch path composed of at least one attribute and at least one category, and a branch path composed of evaluation results The leaf nodes of each branch path of ;
[0026] Step 103: Based on the server evaluation model, evaluate at least one server in the server cluster to obtain an evaluation result for each server in the at least one server;
[0027] Step 104: Perform data scheduling according to the evaluation result of each server in the...
Embodiment 2
[0064] The specific implementation steps of the ID3 decision tree algorithm are as follows: figure 2 Shown:
[0065] First, initialize the parameters to obtain the data set D, attribute set A, category cj, and create a decision tree T;
[0066] Determine whether there is only one category cj in the current data set D, if so, directly add cj to the leaf node of T as a decision node;
[0067] If not, whether the attribute set A is empty, if it is empty, use the cj with the highest proportion in the data set D as the leaf node;
[0068] If it is not empty, calculate the entropy of the data set D, and calculate the entropy of each attribute;
[0069] Select an attribute with the largest information gain as the best classification attribute Ag;
[0070] Judging whether the information gain of the best classification attribute Ag is less than the threshold value, if less, then use the cj with the highest proportion in the data set D as the leaf node;
[0071] If it is not less ...
Embodiment 3
[0076] This embodiment provides a data scheduling system, such as image 3 shown, including:
[0077] A data preprocessing unit 31, configured to determine at least one attribute included in the historical processing performance data and at least one category corresponding to each attribute based on the historical processing performance data of at least one server;
[0078] A model building unit 32, configured to build a server evaluation model based on the historical processing performance data of the at least one server; wherein, the server evaluation model includes: at least one branch path composed of at least one attribute and at least one category, and The leaf nodes of each branch path formed by the evaluation results;
[0079] The server evaluation unit 33 is configured to evaluate at least one server in the server cluster based on the server evaluation model to obtain an evaluation result for each server in the at least one server;
[0080] The scheduling unit 34 is...
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