计算机科学 ›› 2023, Vol. 50 ›› Issue (5): 313-321.doi: 10.11896/jsjkx.220400019
杨乾龙1, 江凌云1,2
YANG Qianlong1, JIANG Lingyun1,2
摘要: 随着云计算技术的不断发展,微服务体系结构受到了越来越多的关注。由于将大型应用程序分割成细粒度的单一服务在开发和维护方面较为方便,许多大型应用程序已经从单体结构发展为微服务体系结构。在微服务架构中,为了提高微服务的可用性,通常采用集群结构的方式部署微服务实例。针对微服务集群中服务器节点随着任务数量的增加而出现负载不均衡的问题,提出了一种基于Xgboost(Extreme Gradient Boosting)的最短预测响应时间负载均衡算法(Shortest Predictive Response Time,SPRT)。首先选取影响任务响应时间的特征参数,然后使用集成学习预测新任务的响应时间,最终将任务分配给预测响应时间最短的服务器节点,以达到服务器节点之间负载均衡的目的。结果表明,相比其他负载算法,所提负载均衡算法在吞吐量、截止率和平均响应时间上都有一定的提升,而且更适用于高并发环境下的微服务集群。
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