Multi-level load forecasting and flexible cloud resource configuring method and monitoring and configuring system

Abstract

The invention relates to a multi-level load forecasting and flexible cloud resource configuring method and a flexible cloud resource configuring system based on monitoring and forecasting. According to the multi-level load forecasting and flexible cloud resource configuring method and the flexible cloud resource configuring system based on monitoring and forecasting, a cloud resource monitoring system is of a two-layer structure, the complexity of the cloud resource monitoring system is reduced, and the load pressure of a single monitoring server is reduced. In a load forecasting process, a forecasting algorithm with the lower time complexity is utilized. Thus, the system resource forecasting cost is reduced. In a flexible resource configuring process, a multi-level forecasting and resource configuring mechanism is utilized to dynamically adjust cloud resources in different time accuracy. Thus, the utilizing rate of the cloud resources is improved.

Description

technical field [0001] The present invention relates to the field of optimal configuration of cloud resources in cloud computing, and more specifically, relates to a method for multi-level load prediction and elastic configuration of cloud resources, and a system for elastic configuration of cloud resources based on monitoring and prediction. Background technique [0002] Cloud computing is an Internet-based computing method in which shared hardware and software resources and information can be provided to computers and other devices on demand. The characteristics that cloud computing services should have are: on-demand self-service, network access anytime and anywhere, a resource pool shared by multiple people, a flexible rapid redeployment mechanism, services that can be monitored and measured, based on virtual The resources and services of modernization technology reduce the computing and storage overhead of the client, and lower the threshold for users to get started. ...

AI Technical Summary

Problems solved by technology

[0005] The problems existing in the existing cloud resource prediction mechanism are as follows: the existing prediction methods mostly use pattern matching and neural network methods for prediction, and the time complexity of the prediction method is too high, which will affect the overall performance of the system, making the prediction process itself a The main system resource overhead; some methods use the AR model to predict, but it ignores the periodic change law of the load

[0006] For the existing methods of cloud resource allocation, the problem is that it does not provide a complete method including cloud resource monitoring, load forecasting and cloud resource elastic configuration, but only discusses a certain aspect of forecasting or resource allocation

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