基于多重分形MLD模型的网络流量预测

L. Hong, Yan Tie, W. Lanlan
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引用次数: 1

摘要

本文提出了一种基于多重分形的未知自仿射信号分类方法,对传统交通信号进行了改进。使用多重分形度量的基本优点包括流量签名的规范化和非常高的压缩比,从而导致更快的实现,并且能够添加新的流量类而无需重新设计流量分类器。混合逻辑动力学(MLD)建模是混合系统建模和控制的一种有效而现实的方法。本文采用MLD框架将多服务器系统建模为一个切换非线性系统。多服务器中的数据流控制被视为MLD系统预测控制的一个案例研究。仿真结果表明,该模型是一个很好的网络流量控制和研究模型。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Network Traffic Prediction Based on Multifractal MLD Model
In this paper, a multifractal approach to the classification of unknown self affine signals is presented as an improvement over traditional traffic signal. The fundamental advantages of using multifractal measures include normalization and a very high compression ratio of a signature of the traffic, thereby leading to faster implementations, and the abiliiy to add new traffic classes without redesigning the traffic classifier. Mixed logical dynamical (MLD) modeling appears as an effective and realistic approach in modeling and control of hybrid systems. In this paper, the MLD framework is used for modeling of a multi-server system as a switched nonlinear system. Control of data flow in multiple servers is considered as a case study for predictive control of MLD systems. It is a good model for network traffic control and research as shown in the simulation.
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