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基于微服务架构的多资源负载均衡优化方法 被引量:6

Multi Resource Load Balancing Optimization Method Based on Microservice Architecture
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摘要 为有效提高网络多资源利用率和执行效率,确保网络多资源负载均衡效果,通过微服务架构研究了多资源负载均衡优化方法。通过数据层、基础层、服务层、业务层以及表示层,建立微服务总体框架结构,动态调度网络多资源服务,降低负载情况,采用微服务架构,构造可扩展的网络拓扑结构。依据负载均衡优化目标建立优化模型,利用多蚁群算法对优化模型进行求解,通过局部和全局更新网络流量之间信息素,实现网络多资源负载均衡优化。结果表明:所提方法的资源负载均衡执行时间仅为10 s,网络资源利用率和负载均衡度分别为89.2%和89.4%。可见所提方法能够对网络多资源负载进行均衡优化,有效提高网络多资源利用率和执行效率,确保资源负载均衡效果。 In order to effectively improve network multi-resource utilization and execution efficiency,and ensure the effect of network multi-resource load balancing,the optimization method of multi-resource load balancing was studied through the microservice architecture.Through the data layer,the basic layer,the service layer,the business layer and the presentation layer,the overall framework of microservice was established.An extensible network topology based on microservice architecture was constructed.The optimization model was established according to the optimization objective of load balancing,the optimization model was solved by multi ant colony algorithm,and the network multi resource load balancing optimization was realized by locally and globally updating the pheromone between network traffic.The results show that the resource load balancing execution time of the proposed method is only 10 s,and the network resource utilization rate and load balancing degree are 89.2%and 89.4%,respectively.It is concluded that the proposed method can balance and optimize the network multi-resource load,effectively improve the network multi-resource utilization and execution efficiency,and ensure the resource load balancing effect.
作者 罗欢 姜唯 刘明伟 徐律冠 LUO Huan;JIANG Wei;LIU Ming-wei;XU Lü-guan(Digital Grid Research Institute,China Southern Power Grid, Guangzhou 510700, China)
出处 《科学技术与工程》 北大核心 2022年第5期1965-1971,共7页 Science Technology and Engineering
基金 河南省科技厅科技攻关支持项目(202102210361,192102310217)。
关键词 微服务架构 网络资源 负载均衡优化 网络拓扑结构 多蚁群算法 microservice architecture network resources load balancing optimization network topology multi ant colony algorithm
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