基于多重分形的VBR视频流量多步预测方法

Multi-Step Prediction of VBR Video Traffic Based on Mutifractal Analysis

摘要: 视频流量的实时预测是进行网络资源优化和端到端QoS策略设计的重要前提.然而，目前基于短相关(SRD)的预测模型并不能对非平稳且具有长相关(LRD)和分形特性的视频流量进行有效的预测.分析发现，通过多重分形尺度间系数的相关性，可以把难以直接预测的LRD流量序列转化为可以用SRD模型预测的短相关序列组.基于多重分形的预测算法合理地利用了原始序列的LRD信息，具有很好的多步预测性能.
- 视频传输 /
- 流量预测 /
- 多重分形
Abstract: Real-time prediction of video source traffic is an important step in network resource management and end-to-end quality-of-service (QoS) strategies. However, together with the long-range-dependence (LRD) and the traffic non-stationarity, it suggests that conventional prediction tools, which only use short-range dependence (SRD), are not appropriate for VBR video traffic prediction. In this paper, by analyzing the correlation structure of multifractal coefficients, the original LRD trace can be converted to a series of SRD sequence in multifractal domain. Because the LRD feature of trace is used, the multi-step performance of proposed multifractal model is much better than traditional methods.
- video transmission /
- traffic prediction /
- multifractal