On the other hand, aggregated traffic modeling defines M2M traffic as one stream from multiple devices.
In future, coactive neuro fuzzy inference system (CANFIS) [19] can be used for network traffic modeling.
Traffic flow is considered as a stochastic process in road traffic modeling.
The rest of users competing with the UUT for system resources are emulated by means of traffic modeling.
We will derive two approaches: a deterministic approach considering full knowledge of the traffic profile throughout the day, and a statistical robust strategy accounting for error traffic modeling.
Using this traffic modeling approach, we propose some probabilistic envelope processes that can be used to represent the resource demand of a traffic stream in performance evaluation studies.
Hence, it is clear from the above results that although ANFIS performs better than ARIMA, this is achieved at the cost of complexity in computation which must be taken into consideration when ANFIS is used for network traffic modeling.
This paper is an attempt to compare the modeling approaches of ARIMA and ANFIS under different scenarios in order to conclude about their comparative suitability for computer network traffic modeling.
To minimize the collinearity problem encountered in inverse traffic modeling it is essential that the monitoring is done at different times to capture significant variations in the street traffic compositions.
The first part of this paper gives a short tutorial survey of Internet traffic modeling, focusing on recent advances in Markov models showing pseudo-LRD (Long Range Dependence) characteristics that match those measured on the Internet.
At the same time, a detailed comparative performance evaluation of ANFIS with other modeling approaches in traffic modeling could not be found in existing literature.
