改进GWO优化SVM的云计算资源负载短期预测研究

被引：65

作者：

徐达宇 ^{[1
,2
]}

丁帅 ^{[2
]}

机构：

[1] 浙江农林大学浙江省林业智能监测与信息技术研究重点实验室

[2] 合肥工业大学计算机与网络系统研究所

来源：

计算机工程与应用 | 2017年 / 07期

基金：

浙江省自然科学基金;

关键词：

云计算; 灰狼优化算法; 支持向量机; 极值优化; 预测;

D O I：

暂无

中图分类号：

TP18 [人工智能理论]; TP3 [计算技术、计算机技术];

学科分类号：

080201 [机械制造及其自动化]; 140502 [人工智能];

摘要：

云计算资源负载短期预测是云计算平台实现资源高效管理和系统安全、稳定运行的重要前提和保障措施之一。为了其提高负载短期预测的预测精度,提出一种改进灰狼搜索算法优化支持向量机的短期云计算资源负载预测模型(EGWO-SVM)。首先介绍灰狼搜索算法(GWO)的基本原理;然后提出基于极值优化的改进GWO模型;最后根据最优参数建立短期资源负载预测模型,并通过仿真实验对EGWO-SVM的性能进行测试。实验结果表明,相对于参比模型,EGWO-SVM能更加准确地刻画云计算短期资源负载的复杂变化趋势,从而有效提升云计算资源负载短期预测的精度。

引用

页码：68 / 73

页数：6

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