Parsimonious Network Traffic Modeling By Transformed ARMA Models
Generating synthetic data traffic, which statistically resembles its recorded counterpart is one of the main goals of network traffic modeling. Equivalently, one or several random processes shall be created, exhibiting multiple prescribed statistical measures. In this paper, we present a framework e...
| Main Authors: | , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
IEEE
2014-01-01
|
| Series: | IEEE Access |
| Subjects: | |
| Online Access: | https://ieeexplore.ieee.org/document/6710106/ |
| _version_ | 1818332030858952704 |
|---|---|
| author | Markus Laner Philipp Svoboda Markus Rupp |
| author_facet | Markus Laner Philipp Svoboda Markus Rupp |
| author_sort | Markus Laner |
| collection | DOAJ |
| description | Generating synthetic data traffic, which statistically resembles its recorded counterpart is one of the main goals of network traffic modeling. Equivalently, one or several random processes shall be created, exhibiting multiple prescribed statistical measures. In this paper, we present a framework enabling the joint representation of distributions, autocorrelations and cross-correlations of multiple processes. This is achieved by so called transformed Gaussian autoregressive moving-average models. They constitute an analytically tractable framework, which allows for the separation of the fitting problems into subproblems for individual measures. Accordingly, known fitting techniques and algorithms can be deployed for the respective solution. The proposed framework exhibits promising properties: 1) relevant statistical properties such as heavy tails and long-range dependences are manageable; 2) the resulting models are parsimonious; 3) the fitting procedure is fully automatic; and 4) the complexity of generating synthetic traffic is very low. We evaluate the framework with traced traffic, i.e., aggregated traffic, online gaming, and video streaming. The queueing responses of synthetic and recorded traffic exhibit identical statistics. This paper provides guidance for high-quality modeling of network traffic. It proposes a unifying framework, validates several fitting algorithms, and suggests combinations of algorithms suited best for specific traffic types. |
| first_indexed | 2024-12-13T13:29:16Z |
| format | Article |
| id | doaj.art-078baca164514a45bafe3a7ddd0c3c74 |
| institution | Directory Open Access Journal |
| issn | 2169-3536 |
| language | English |
| last_indexed | 2024-12-13T13:29:16Z |
| publishDate | 2014-01-01 |
| publisher | IEEE |
| record_format | Article |
| series | IEEE Access |
| spelling | doaj.art-078baca164514a45bafe3a7ddd0c3c742022-12-21T23:44:13ZengIEEEIEEE Access2169-35362014-01-012405510.1109/ACCESS.2013.22977366710106Parsimonious Network Traffic Modeling By Transformed ARMA ModelsMarkus Laner0Philipp Svoboda1Markus Rupp2Institute of Telecommunications, Vienna University of Technology, Vienna, AustriaInstitute of Telecommunications, Vienna University of Technology, Vienna, AustriaInstitute of Telecommunications, Vienna University of Technology, Vienna, AustriaGenerating synthetic data traffic, which statistically resembles its recorded counterpart is one of the main goals of network traffic modeling. Equivalently, one or several random processes shall be created, exhibiting multiple prescribed statistical measures. In this paper, we present a framework enabling the joint representation of distributions, autocorrelations and cross-correlations of multiple processes. This is achieved by so called transformed Gaussian autoregressive moving-average models. They constitute an analytically tractable framework, which allows for the separation of the fitting problems into subproblems for individual measures. Accordingly, known fitting techniques and algorithms can be deployed for the respective solution. The proposed framework exhibits promising properties: 1) relevant statistical properties such as heavy tails and long-range dependences are manageable; 2) the resulting models are parsimonious; 3) the fitting procedure is fully automatic; and 4) the complexity of generating synthetic traffic is very low. We evaluate the framework with traced traffic, i.e., aggregated traffic, online gaming, and video streaming. The queueing responses of synthetic and recorded traffic exhibit identical statistics. This paper provides guidance for high-quality modeling of network traffic. It proposes a unifying framework, validates several fitting algorithms, and suggests combinations of algorithms suited best for specific traffic types.https://ieeexplore.ieee.org/document/6710106/Traffic modelingtransformed GaussianARMA modelparsimoniousness |
| spellingShingle | Markus Laner Philipp Svoboda Markus Rupp Parsimonious Network Traffic Modeling By Transformed ARMA Models IEEE Access Traffic modeling transformed Gaussian ARMA model parsimoniousness |
| title | Parsimonious Network Traffic Modeling By Transformed ARMA Models |
| title_full | Parsimonious Network Traffic Modeling By Transformed ARMA Models |
| title_fullStr | Parsimonious Network Traffic Modeling By Transformed ARMA Models |
| title_full_unstemmed | Parsimonious Network Traffic Modeling By Transformed ARMA Models |
| title_short | Parsimonious Network Traffic Modeling By Transformed ARMA Models |
| title_sort | parsimonious network traffic modeling by transformed arma models |
| topic | Traffic modeling transformed Gaussian ARMA model parsimoniousness |
| url | https://ieeexplore.ieee.org/document/6710106/ |
| work_keys_str_mv | AT markuslaner parsimoniousnetworktrafficmodelingbytransformedarmamodels AT philippsvoboda parsimoniousnetworktrafficmodelingbytransformedarmamodels AT markusrupp parsimoniousnetworktrafficmodelingbytransformedarmamodels |