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Container resource supply method and system based on stochastic regression forest model

A technology that returns to forests and containers. It is applied in the field of microservice gateways and can solve problems such as heavy load and long response time.

Active Publication Date: 2018-12-11
SUN YAT SEN UNIV
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AI Technical Summary

Problems solved by technology

In the case of dynamic load changes, based on the actual response time and access volume of users, quickly expand the microservices that change to high loads to solve the overload and long response time caused by a large number of users accessing the same service together problems; and when the server provides a large number of container resources for a high-load service cluster, when the load suddenly decreases, it can quickly reclaim resources for micro-services under low load, so as to ensure user service quality and optimize resource utilization.

Method used

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  • Container resource supply method and system based on stochastic regression forest model
  • Container resource supply method and system based on stochastic regression forest model

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Experimental program
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Embodiment 1

[0045] Such as figure 1 As shown, a container resource supply method based on the random regression forest model includes the following steps:

[0046]Step 1. The data collector periodically collects statistics on the record details of user access from the gateway. Due to uncontrollable factors such as the network, the collected data may fluctuate greatly, which cannot effectively reflect the current status of the service. Therefore, Kalman is used. Filtering, which performs real-time filtering processing on the collected response time and traffic, making the data smoother and better reflecting the status of the service. Through preprocessing such as classification and weighting of the record details, the response time and visit volume of each service are counted.

[0047] Step 2. The service scheduler periodically requests the data collector for the response time and visit volume of each service, and then uses the autoregressive time series method to build a corresponding pr...

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Abstract

The invention provides a container resource supply method based on a stochastic regression forest model. Under the condition of dynamic changes of load, based on the actual response time and visitingquantity of users, the invention rapidly expands the capacity of micro-service with high load to solve the problem of overload and long response time caused by a large number of users accessing the same service together. When the server provides a large number of container resources for the high load service cluster and the load decreases suddenly, the resources for the low load micro-service canbe recycled quickly to ensure the quality of service and optimize the use of resources.

Description

technical field [0001] The present invention relates to the technical field of microservice gateways, and more specifically, to a container resource supply method and system based on a random regression forest model. Background technique [0002] With the rapid development of the Internet, the cluster technology is constantly updated, and the disadvantages of the traditional monolithic application continue to appear over time. The microservice architecture (Microservice) conforms to the idea of ​​Internet application design. Its fine-grained module division, service-oriented interface encapsulation, and lightweight (Restful) communication interaction have the following advantages: [0003] (1) Each service is relatively simple and only focuses on one business function; [0004] (2) The microservice architecture is loosely coupled, which can provide higher flexibility; [0005] (3) Microservices can be developed through the best and most suitable different programming langu...

Claims

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Application Information

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Patent Type & Authority Applications(China)
IPC IPC(8): G06F9/455G06F9/50G06K9/62
CPCG06F9/45558G06F9/5027G06F9/5077G06F18/24323Y02D10/00
Inventor 余阳吕京泽严有强
Owner SUN YAT SEN UNIV
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