Flexible allocation method and device for container cloud resources, computer equipment and medium

Abstract

The embodiment of the invention discloses a container cloud resource elastic allocation method and device, computer equipment and a medium. The method comprises the following steps: acquiring a user request arrival rate of a container cloud processing platform; capturing a Qos index of an application related to the user request, wherein the Qos index comprises a CPU utilization rate, a memory utilization rate and request response data of a Pod service processing unit; predicting the request arrival rate at the next moment by adopting a prediction model according to the request arrival rate; calculating the number of Pod service units required by prediction according to the predicted request arrival rate at the next moment; and carrying out resource allocation according to the predicted number of the required Pod service units. According to the method and the device, the resource elastic allocation is performed according to the predicted number of the required Pod service units, so that the application resources deployed on the container cloud cluster are responded and allocated in advance before the peak arrives, the response time is reduced, the user access experience and the stability of platform service are improved, and the resource waste is avoided.

Description

technical field [0001] The present invention relates to the field of cloud computing, and more particularly to a method, device, computer equipment and medium for elastic allocation of container cloud resources. Background technique [0002] The rapid development of urbanization has led to increasingly prominent disaster safety problems, and the total number of urban disaster accidents and the degree of damage have increased significantly. Improving the ability of urban disaster prevention and control has become one of the important missions of urban development. Cloud resource elastic allocation technology is one of the key technologies of disaster emergency cloud computing center. By establishing a reasonable resource scheduling and allocation method, it can effectively solve the problems of application stability and performance in high concurrency scenarios. [0003] With the exponential growth of data scale and user requests, higher service quality requirements are put f...

AI Technical Summary

Problems solved by technology

[0004] Kubernetes is currently the mainstream container cloud resource management platform. The built-in responsive HPA elastic scaling strategy has problems such as resource oversupply, undersupply, and response delay. Oversupply will increase costs and waste resources, while undersupply and response delay will lead to service The level agreement (SLA, service level agreement) is violated, the service quality (Qos, quality of service) is reduced, and the user access experience is affected

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