Elastic scheduling method and system

Abstract

The embodiment of the invention provides an elastic scheduling method and system, and the method comprises the steps: collecting a time sequence index of a key feature of a service in a preset time period from a server, wherein the time sequence index of the key feature is an index value sequence of the key feature with a time index; respectively calculating the weight of each key feature and the quantile of each key feature according to the time sequence indexes of the collected key features; and according to the time sequence index newly generated by any key feature of the service, adopting the quantile of the key feature and the weight of the key feature to judge the time sequence index newly generated by the key feature, obtaining a final scheduling behavior decision, and carrying out scheduling according to the final scheduling behavior decision, wherein the scheduling behavior decision comprises one of the following decisions: index abnormity alarm and automatic capacity expansion and contraction. The time sequence indexes of the key features in the historical data are processed to judge whether to schedule or not, so that the inaccuracy of judging whether to schedule or not due to relatively strong subjective factors of manually determining the threshold value is avoided.

Description

technical field [0001] The invention relates to the field of intelligent operation and maintenance, in particular to a flexible scheduling method and system. Background technique [0002] In the current conventional elastic scheduling system, the method of artificially estimating the threshold point of key indicators for expansion and contraction is usually adopted. The disadvantage of this strategy method is that the accuracy of the threshold value mainly depends on human experience and judgment, and the subjective factor is strong. Contents of the invention [0003] Embodiments of the present invention provide a method and system for flexible scheduling, which judges whether to schedule by processing the timing indicators of key features in historical data, and avoids inaccurate judgment of whether to schedule due to strong subjective factors of manually determining the threshold. [0004] To achieve the above purpose, on the one hand, an embodiment of the present invent...

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Problems solved by technology

[0002] In the current conventional elastic scheduling system, the method of artificially estimating the threshold point of key indicators for capacity expansion and contraction is usually adopted. The disadvantage of this strategy method is that the accuracy of the threshold value mainly depends on human experience judgment, and the subjective factors are relatively strong.

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