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Computing resource scheduling method and device and storage medium

A technology of computing resources and scheduling methods, applied in the Internet field, can solve the problems of lack of cloud computing platform, waste of computing resources, high cost, improve the accuracy of resource utilization and allocation, realize dynamic automatic scheduling, and improve management efficiency. Effect

Pending Publication Date: 2022-02-08
中国电信股份有限公司甘肃分公司
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  • Summary
  • Abstract
  • Description
  • Claims
  • Application Information

AI Technical Summary

Problems solved by technology

[0002] With the development of Internet technology, there are more and more application requirements for resource management by setting up cloud computing platforms. At present, the industry lacks automatic and intelligent control and management methods for cloud computing platforms. The conventional control and management methods adopted Firstly, the user puts forward the work demand for cloud computing platform resource allocation and scheduling, and then the system approves the demand application, and finally relies on the management personnel to allocate and schedule the computing resources of the cloud computing platform according to their own work experience. The allocation and scheduling of computing resources is costly and lacks reliable evaluation standards. It is easy to waste computing resources due to inexperience and misjudgment, which restricts the resource management efficiency of the cloud computing platform and hinders the management level of the cloud computing platform. improvement

Method used

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  • Computing resource scheduling method and device and storage medium
  • Computing resource scheduling method and device and storage medium

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Embodiment 1

[0034] Embodiments of the present application provides a resource scheduling method for calculating, as figure 1 Distance figure 1 Present application flow chart of a method of calculating the resource scheduling according to an embodiment, the computing resource scheduling method comprising:

[0035] S101, extracts key indicators cloud computing platform services and services from the log data, the acquired data bearer service service performance;

[0036] Wherein the key indicators include: traffic, frequency the API interface calls, the user request at least one indicator of the number of data, the performance data comprising traffic load, the processor load, memory load, disk load, the load network IO at least one of performance data.

[0037] In practical application scenario of the present embodiment, in order to improve the accuracy and comprehensiveness of resource scheduling, extraction cloud computing platform services business key indicators and performance data from th...

Embodiment 3

[0072] Based on the image processing method provided by the above embodiment, the present application provides a storage medium that stores a computer program on which the processor performs a computer program stored on the storage medium, and implements the present application In any of the first aspect, the method of calculating a resource scheduling method comprising: extracting a key indicator of a cloud computing platform service service from the log data to obtain the business service performance data; wherein the key indicators comprise: Business, API interface call frequency, at least one indicator data in the user request number, the performance data includes at least one of the service load, a processor load, a memory load, a disk load, a network IO load;

[0073] The extracted key indicators and performance data are sample data, and the processing resource prediction model and the business support capability index model are constructed by the machine learning algorithm....

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Abstract

The invention provides a computing resource scheduling method and device and a storage medium, and the computing resource scheduling method comprises the steps: extracting a key index of a cloud computing platform business service from log data, obtaining performance data bearing the business service, taking the extracted key index and performance data as sample data, and through a machine learning algorithm, constructing a processing resource prediction model and a service support capability index model, and allocating and scheduling computing resources of the cloud computing platform by using the processing resource prediction model and the service support capability index model according to the key indexes and performance data of the current cloud computing platform, resource waste and scheduling errors caused by resource scheduling depending on artificial experience judgment are avoided, the labor cost is reduced, the management efficiency of the cloud computing platform is improved, the dynamic automatic scheduling of the cloud computing platform resources is realized, and the resource utilization rate and the distribution accuracy are improved.

Description

Technical field [0001] The present application relates to the field of Internet technologies, particularly to a computing resource scheduling method, apparatus and a storage medium. Background technique [0002] With the development of Internet technology, cloud computing by providing a platform for applications under the jurisdiction of more and more resources, lack of computing automation platform for cloud intelligent control and management tools in the industry at present, conventional methods to control and manage its use by first propose computing platform resource allocation and scheduling of the cloud work demand by the user, and then through the system to the needs of the application for approval, the final by virtue of managers based on their experience in the allocation and scheduling computing computing resource platform for cloud, which kinds of high-allocation and scheduling cost of computing resources, and the lack of reliable assessment criteria, easily due to lac...

Claims

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Application Information

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Patent Type & Authority Applications(China)
IPC IPC(8): G06F9/50G06K9/62G06N20/00
CPCG06F9/505G06N20/00G06F9/5077G06F18/23
Inventor 焦颉韩瑞萍郭延军张励陈学英刘斌文彦杰刘宝丽杨毛勇何颖任彦辉芦睿珩李学兰唐俊闫文贵刘凯敏
Owner 中国电信股份有限公司甘肃分公司