双层隐马尔可夫链的突发流合成

网络流模型被广泛用于构建网络与网络服务的测试环境,其准确性直接影响各种业务的性能评估结果及在实际网络环境中的鲁棒性.随着电子商务及新型网络应用的普及,突发流现象已经成为现代互联网的主要特征之一.针对平稳网络流而设计的传统网络流模型已经难以有效地描述现代网络中突发流的时间结构性及统计属性,从而不能准确反映现代网络流的行为特征.为此,提出一种新的结构化双层隐马尔可夫模型用于模拟实际网络环境下的突发流,并设计了有效的模型参数推断算法及突发流合成方法.该模型通过结构化的2层隐马尔可夫过程描述突发流并实现仿真合成,使合成流可以重现实际突发流的时间结构性、统计特性及自相似性.实验表明,该模型可以有效合成突发流.

A doubly hidden Markov model for synthesizing bursty workloads

Network traffic models have been widely used to build the test environment for networks and network services.Their accuracy directly impacts the performance evaluation results of various services and their robustness in the actual network environment.With the popularity of e-commerce and new network applications,the burst traffic phenomenon has become one of the main features of the modern internet.Traditional traffic models designed for stationary network traffic have difficulty in effectively describing the temporal structure and statistical properties of burst traffic of modern networks,which causes them not to be able to accurately reflect the actual network environment.In this paper,a new structural doubly hidden Markov model was proposed to characterize the practical burst traffic in a real network environment.Efficient algorithms for inference of model parameters and synthesis of the burst workload were also introduced.Based on the hierarchical structure,the proposed model can reproduce the similar temporal structure,statistical properties,and self-similarity of the real burst traffic.The proposed model includes two hidden Markov processes.The parent Markov state process was used to describe the large-scale trends or phases of burst traffic.The child Markov process was used to describe the small-scale fluctuations that happen during a given phase of the arrival process.Experiments were implemented to validate the proposed model.