Cloud data center load prediction method based on LSTM (Long Short-Term Memory)

Abstract

The invention discloses a cloud data center load prediction method based on LSTM (Long Short-Term Memory), and aims to solve the problem that optimal utilization can not be obtained by the limited calculation resources of a cloud data center. The method comprises the following steps that: taking the mass historical records of the cloud data center as a basis to manufacture a training sample and atesting sample; in addition, constructing a neural network connected by LSTM units; continuously inputting training samples on batch to obtain an output value. A neural network optimization algorithmadopts a new adaptation moment estimation method, parameters in each unit are continuously updated through iterative training, and global optimum is realized after training is finished, only the testing sample needs to be input into the network to obtain the next prediction value of a sample sequence; and if an input sequence is continuously updated by the prediction value, a prediction value sequence in one future period of time can be obtained.

Description

technical field [0001] The invention relates to the technical field of cloud computing, in particular to a cloud data center load forecasting method based on a long short-term memory network. Background technique [0002] Cloud computing is an increase, use and delivery model of Internet-based related services, usually involving the provision of dynamically scalable and often virtualized resources through the Internet, which can provide on-demand computing to a large number of users with different priority levels through the network resources and calculation results. The resources in the cloud data center usually adopt a pay-as-you-go mode to dynamically provide services to users. [0003] According to the definition of the National Institute of Standards and Technology (NIST), this mode provides available, convenient, and on-demand network access to a shared pool of configurable computing resources (resources include networks, servers, storage, application software, servi...

AI Technical Summary

Problems solved by technology

In view of the current problem that the resources in the cloud data center cannot be allocated optimally, it is necessary to propose a model based on the LSTM neural network, relying on a large amount of historical data for each priority task request sequence and unit request resource application sequence to train and complete An optimized method to accurately predict these two indicators for a period of time in the future

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