摘要
大数据时代的快速发展和大数据战略的明确提出,使得Web服务器集群将面临更加复杂和严峻的负载挑战。传统的负载均衡算法存在着明显的局限性。提出了一种基于强挂起弱预测机制的负载均衡模型,该模型利用强挂起机制和基于层次分析的三次指数平滑预测算法进行负载均衡动态调度。实验结果表明该模型在系统瞬时性能异常、高并发和重负载交互情况下的负载均衡效果优于传统负载均衡算法。
With the rapid development of big data and big data strategy is put forward,the Web server cluster system will face more severe challenges in terms of load.The traditional load balancing algorithm has obvious limitations.This paper proposes a dynamic load-balancing model based on the SSAWF mechanism.The model uses strong suspend mechanism and cubic exponential smoothing method prediction based on AHP algorithm for dynamic load balancing scheduling.Results of the experiments show that the model is better than the traditional load balancing under abnormal system transient performance,high concurrency and high system load interaction.
作者
刘迪
朱立谷
张雷
冯东煜
LIU Di;ZHU Ligu;ZHANG Lei;FENG Dongyu(Communication University of China, Beijing 100024, China;Beijing Key Laboratory of Big Data in Security & Protection Industry, Beijing 100024, China)
出处
《计算机工程与应用》
CSCD
北大核心
2017年第10期90-95,123,共7页
Computer Engineering and Applications
基金
国家自然科学基金项目(No.6137006)
关键词
WEB服务器集群
负载均衡
强挂起弱预测
层次分析法
三次指数平滑法
Web server cluster
load-balancing
Strong Suspend And Weak Forecast(SSAWF)
Analytic Hierarchy Process (AHP)
cubic exponential smoothing method
