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A service discovery and client load balancing method based on a service registration center

A service registration and service discovery technology, applied in the field of micro-service architecture application system, can solve problems such as load balancing easily becoming a bottleneck, system impact disaster, etc.

Inactive Publication Date: 2019-01-22
NARI INFORMATION & COMM TECH
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  • Summary
  • Abstract
  • Description
  • Claims
  • Application Information

AI Technical Summary

Problems solved by technology

The main problem of centralized load balancing is the single point problem. All service call traffic is load balanced. When the number of services and call volume is large, load balancing tends to become a bottleneck, and once the load balancing fails, the impact on the entire system will be catastrophic. sex
In addition, load balancing adds a hop (hop) between the service consumer and the service provider, which has a certain performance overhead

Method used

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  • A service discovery and client load balancing method based on a service registration center
  • A service discovery and client load balancing method based on a service registration center
  • A service discovery and client load balancing method based on a service registration center

Examples

Experimental program
Comparison scheme
Effect test

example B

[0074] · Example B: 220+(230-40)=410

example C

[0075] · Example C: 410+(230-80)=560

example D

[0076] · Example D: 560+(230-100)=690

[0077] The weight value only indicates the upper limit of the weight range of each instance, not the priority of a certain instance, so the larger the value, the greater the probability of being selected. The above example actually constructs 4 different intervals for these 4 instances. The lower limit of each instance is the upper limit of the previous instance, and the upper limit of each instance is calculated above and stored in the List The weight value for , where the lower bound of the first instance defaults to zero. Therefore, according to the weight calculation results of the above example, we can get the weight range of each instance:

[0078] · Instance A: [0,220]

[0079] · Instance B: (220,410]

[0080] · Instance C: (410,560]

[0081] · Example D: (560, 690)

[0082] The width of each interval is: the total average response time - the average response time of the instance, so the shorter the average response time of t...

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Abstract

The invention discloses a service discovery and client load balancing method based on a service registration center. The method includes the following steps: obtaining an application service unique identifier from a call request; aservice consumer invokes aservice instance query interface of the service registry, and obtains all available service instances of the set application service based on the RESTful service invocation; calculating the response time weight of the instance server, and generating the weighted interval data according to the average response time of the history invoking server; obtaining concrete application service instances. The service consumer's interceptor selects an application service instance based on the load algorithm of response time; calculating the serviceinvocation time, calculating the average response time, the minimum response time, the maximum response time and the invocation times of the set application server. The invention can improve the utilization rate of resources, improve the availability of the system, and the cost is low.

Description

technical field [0001] The invention relates to a service discovery and client load balancing method based on a service registration center, and belongs to the technical field of microservice architecture application systems. Background technique [0002] With the advancement of cloud technology and the growth of services, microservice architecture has attracted more and more people's attention. Microservices is also a new technology for deploying applications and services in the cloud. Although there are many different debates, the microservice architectural pattern is providing great help for agile deployment and implementation of complex enterprise applications. [0003] In cloud-based microservice applications, the network addresses (IP Address and Port) of service instances are dynamically allocated, and due to factors such as system elastic scaling, service failure, and service upgrades, the number of running instances of some services is also dynamically changing . ...

Claims

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Application Information

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IPC IPC(8): H04L29/08
CPCH04L67/1008H04L67/101H04L67/51
Inventor 郭伟胡牧刘士进刘一田程旭康智辉李郑赵洁何翔曾会峰王婷吴海超
Owner NARI INFORMATION & COMM TECH
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