计算机科学 ›› 2020, Vol. 47 ›› Issue (9): 246-251.doi: 10.11896/jsjkx.190800008
苏畅, 张定权, 谢显中, 谭娅
SU Chang, ZHANG Ding-quan, XIE Xian-zhong, TAN Ya
摘要： 随着5G研究的深入和商用的推进,出现了各式各样的挑战,其中,5G通信系统的资源管理对于5G网络的研究来说是一个关键性的挑战。网络功能虚拟化技术为5G的实现提供了关键支撑,同时也为5G的资源管理问题引入了新的研究方向,但是网络功能虚拟化场景中的资源管理是一个比较复杂问题。特别地,虚拟网络功能的不同放置位置会为其性能带来不同的影响。文中首先对网络功能虚拟化的资源分配方法及放置对性能的影响进行了分析和研究,在此基础上,主要根据知识定义网络所提出的范例,探讨了将机器学习技术应用于虚拟网络功能内存资源管理的研究,构建神经网络学习模型,预测内存资源消耗。其次,重点对输入流量的特征进行提取,流量主要由一组特征表示,这些特征代表了从数据链路层到传输层的小批次信息,其中的内存消耗是从虚拟机管理程序的性能监测工具上得出的批量的平均内存消耗。最后,利用神经网络模型预测内存资源消耗,从而达到对内存资源进行管理的目的。
中图分类号:
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