Method for predicting resource performance of cloud server based on LSTM-ACO model

Abstract

The invention discloses a method for predicting the resource performance of a cloud server based on an LSTM-ACO model. The method comprises the steps of firstly carrying out the preprocessing of time sequence data, and mapping original sequence data to a [0, 1] interval; then determining an LSTM model, training and predicting existing data, and optimizing the LSTM model by using an ant colony algorithm; and finally, inputting the prediction result of the LSTM model for the data of the moment t and the data of the moments t-1, t-2,..., t-n into the LSTM-ACO model, and predicting the data of the moment t. According to the method for predicting the resource performance of the cloud server based on the LSTM-ACO model, the problem that a traditional prediction method is not high in precision in the prediction process is solved, the LSTM parameters are optimized through ACO, the problem that the model is caught in a local optimal solution is avoided, and the prediction convergence speed is increased; and finally, cloud server resource and performance prediction is realized, and the software aging phenomenon is predicted more accurately.

Description

technical field [0001] The invention belongs to the technical field of time series prediction, in particular to a method for predicting cloud server resource performance based on a Long Short-Term Memory (LSTM) cyclic neural network and ant colony optimization (ACO) model. Background technique [0002] With the development of modern computer technology and cloud computing, the use of cloud servers is becoming more and more common. Cloud servers are characterized by long-running, high complexity, and frequent resource exchange, which increases the risk of resource exhaustion and software system anomalies and failures. With the accumulation of failures and resource consumption, the cloud server system will experience slow performance degradation, increased failure rate and even crash. This phenomenon is called "software aging". The main causes of software aging include the consumption of operating system resources, the destruction of data, and the accumulation of errors. Th...

AI Technical Summary

Problems solved by technology

It solves the problem of low prediction accuracy of traditional prediction methods for cloud server resource performance data with large fluctuations

And a time series data calculation method using ant colony algorithm to optimize parameters is proposed, which overcomes the problems of easy to fall into local optimal solution, slow convergence speed and instability in the process of model prediction

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