EROS-5: Gerador de Tráfego Sintético para Redes 5G
Abstract
This paper presents the EROS-5 tool (Synthetic Traffic Generator for 5G Neworks), which allows simulating a variety of applications such as video streaming, VoIP (Voice over IP), Web and IoT, in 5G network scenarios. EROS- 5 is an open source and configurable tool that allows the easy coupling of new mathematical models to generate new types of traffic. The traffic generated is in the form of time series in JSON format, which presents the necessary flexibility to be used by several simulators such as ns-3 and OMNET ++.
References
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Mattos, C. I., Ribeiro, E. P., Fernandez, E. M. G., and Pedroso, C. M. (2012). An unifiedVoIP model for workload generation. Multimedia Tools and Applications, 70(3):2309-2329.
Pries, R., Magyari, Z., and Tran-Gia, P. (2012). An http web traffic model based on thetop one million visited web pages. In Proceedings of the 8th Euro-NF Conference onNext Generation Internet NG! 2012, pages 133-139.
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Sivanathan, A., Sherratt, D., Gharakheili, H. H., Radford, A., Wijenayake, C., Vishwa-nath, A., and Sivaraman, V. (2017). Characterizing and classifying iot traffic insmart cities and campuses. In 2017 IEEE Conference on Computer CommunicationsWorkshops (INFOCOM WKSHPS), pages 559-564.
Waldmann, S., Miller, K., and Wolisz, A. (2017). Traffic model for http-based adap-tive streaming. In 2017 IEEE Conference on Computer Communications Workshops(INFOCOM WKSHPS), pages 683-688.
Zheleva, M., Schmitt, P., Vigil, M., and Belding, E. (2013). The increased bandwidthfallacy. In Proceedings of the 4th Annual Symposium on Computing for Development- ACM DEV-4 13. ACM Press.
5G-Range (2018). Application and requirements report. Technical report.nttp://5g-range.eu/wp-content /uploads/2019/02/5G-RangeD2.1 V3.pdf.
Chen, Y., Farley, T., and Ye, N. (2004). Qos requirements of network applications on theinternet. Inf. Knowl. Syst. Manag., 4(1):55-76.
CTTC (2019). The first release of 5g-lena is available. nttp://www.cttc.es/the-first-release-of-5g-lena-is-available/.
Da Silva, W. R., Oliveira, L., Kumar, N., Rabêlo, R. A. L., Marins, C. N. M., and Rodri-gues, J.J. P.C. (2018). An internet of things tracking system approach based on loraprotocol. In 2018 IEEE Global Communications Conference (GLOBECOM), pages1-7.
Ericsson (2019). Ericsson mobility report. Technical report, Ericsson. https://www.ericsson.com/4acdle/assets/local/mobility-report/documents/2019/emr-november-2019.pdf.
Gupta, V., Devar, S. K., Kumar, N. H., and Bagadi, K. P. (2017). Modelling of iot trafficand its impact on lorawan. In GLOBECOM 2017 - 2017 IEEE Global CommunicationsConference, pages 1-6.
Hoßfeld, T., Metzger, F., and Heegaard, P. E. (2018). Traffic modeling for aggregatedperiodic iot data. In 2018 21st Conference on Innovation in Clouds, Internet andNetworks and Workshops (ICIN), pages 1-8.
Hyoung-Kee Choi and Limb, J. O. (1999). A behavioral model of web traffic. In Procee-dings. Seventh International Conference on Network Protocols, pages 327-334.
Jain, R. (1991). The art of computer systems performance analysis - techniques for expe-rimental design, measurement, simulation, and modeling. Wiley professional compu-ting. Wiley.
Kettani, H. and Gubner, J. A. (2003). Estimation of the long-range dependence parameterof fractional arima processes. In 28th Annual IEEE International Conference on LocalComputer Networks, 2003. LCN "03. Proceedings., pages 307-308.
Leland, W. E., Taqqu, M. S., Willinger, W., and Wilson, D. V. (1994). On the self-similarnature of ethernet traffic (extended version). IEEE/ACM Transactions on Networking,2(1):1-15.
Maternia, M., El Ayoubi, S. E., Fallgren, M., Spapis, P., Qi, Y., Martín-Sacristán, D.,Carrasco, , Fresia, M., Payaró, M., Schubert, M., Bedo, J. S., and Kulkarni, V. (2016).5G PPP Use Cases and Performance Evaluation Models. 5GPPP.
Mattos, C. I., Ribeiro, E. P., Fernandez, E. M. G., and Pedroso, C. M. (2012). An unifiedVoIP model for workload generation. Multimedia Tools and Applications, 70(3):2309-2329.
Pries, R., Magyari, Z., and Tran-Gia, P. (2012). An http web traffic model based on thetop one million visited web pages. In Proceedings of the 8th Euro-NF Conference onNext Generation Internet NG! 2012, pages 133-139.
Sandvine (2018). 2018 global internet phenomena report. https: //www. sandvine.com/2018-internet-phenomena-report.
Sivanathan, A., Sherratt, D., Gharakheili, H. H., Radford, A., Wijenayake, C., Vishwa-nath, A., and Sivaraman, V. (2017). Characterizing and classifying iot traffic insmart cities and campuses. In 2017 IEEE Conference on Computer CommunicationsWorkshops (INFOCOM WKSHPS), pages 559-564.
Waldmann, S., Miller, K., and Wolisz, A. (2017). Traffic model for http-based adap-tive streaming. In 2017 IEEE Conference on Computer Communications Workshops(INFOCOM WKSHPS), pages 683-688.
Zheleva, M., Schmitt, P., Vigil, M., and Belding, E. (2013). The increased bandwidthfallacy. In Proceedings of the 4th Annual Symposium on Computing for Development- ACM DEV-4 13. ACM Press.
Published
2020-12-07
How to Cite
SOARES, Rafael Amaral; FERREIRA, Gabriel Carvalho; SOLIS, Priscila America; CAETANO, Marcos Fagundes.
EROS-5: Gerador de Tráfego Sintético para Redes 5G. In: DEMO SESSION - BRAZILIAN SYMPOSIUM ON COMPUTER NETWORKS AND DISTRIBUTED SYSTEMS (SBRC), 38. , 2020, Rio de Janeiro.
Anais [...].
Porto Alegre: Sociedade Brasileira de Computação,
2020
.
p. 41-48.
ISSN 2177-9384.
DOI: https://doi.org/10.5753/sbrc_estendido.2020.12400.
