Rapid model parameterization from traffic measurements
Rapid model parameterization from traffic measurements
复制标题
根据流量测量快速进行模型参数化
DOI：
10.1145/643114.643117
复制
发表时间：
2002
期刊：
影响因子：
--
通讯作者：
J. Heidemann
中科院分区：
文献类型：
--
作者：
Kun;J. Heidemann
模拟和分析的实用性在很大程度上依赖于良好的网络流量模型。虽然网络流量随着时间的推移不断变化，但现有方法从收集跟踪、分析数据到最终生成和实施模型通常需要数年时间。在本文中，我们描述了支持根据实时网络测量对流量模型进行快速参数化的方法和工具。我们没有将测量到的流量视为时间序列的统计数据，而是利用跟踪来估计最终用户行为和网络状况，以生成应用程序级模拟模型。我们还表明多尺度分析技术有助于调试和验证模型。为了演示我们的方法，我们开发了 Web 和 FTP 流量的结构源级模型，并通过将模拟输出与原始跟踪进行比较来评估其准确性。我们还将我们的工作与现有的流量生成工具进行比较，并表明我们的方法在捕获流量的异构性方面更加灵活。最后，我们自动化并集成了从跟踪分析到模型验证的过程，以便根据新数据轻松进行模型参数化。
The utility of simulations and analysis heavily relies on good models of network traffic. While network traffic constantly is changing over time, existing approaches typically take years from collecting trace, analyzing the data to finally generating and implementing models. In this paper, we describe approaches and tools that support rapid parameterization of traffic models from live network measurements. Rather than treating measured traffic as a time-series of statistics, we utilize the traces to estimate end-user behavior and network conditions to generate application-level simulation models. We also show multi-scaling analytic techniques are helpful for debugging and validating the model. To demonstrate our approaches, we develop structural source-level models for web and FTP traffic and evaluate their accuracy by comparing the outputs of simulation against the original trace. We also compare our work with existing traffic generation tools and show our approach is more flexible in capturing the heterogeneity of traffic. Finally, we automate and integrate the process from trace analysis to model validation for easy model parameterization from new data.