基于强挂起弱预测机制的负载均衡模型研究

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。

Research of load-balancing model based on SSAWF mechanism

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.