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Modeling and Characterization Traffic Voice, Video, Data and Telemetry under Pareto Distribution-Oriented Networks have on Power Line Communications


Affiliations
1 Escuela de Ciencias Basicas Tecnologia e Ingenieria (ECBTI), Universidad Nacional Abierta y a Distancia; Carrera 27 Nro. 40-43. Bucaramanga, Colombia
2 Universidad de Sucre, Cra28 # 5-267, Sincelejo, Colombia
 

Background/Objectives: To date, it has not found a traffic model that allows characterizing different kinds of service, which describe one self-similar behavior HAN (Home Area Network) networks on PLC (Power Line Communications). The aim of the article is to propose a model to characterize traffic classes such as voice, video, data and telemetry, describing a behavior autosimilar supported on the Pareto distribution. Methods: The Pareto distribution is a distribution type that is well aligned with autosimilar type of traffic. Pareto distribution is used as a source of network traffic, which has a bursty behavior, which can be repeated continuously within the network. To estimate the parameters describing the Pareto distribution for each class of service, a theoretical and experimental analysis was performed to determine the minimum rate, average and maximum, required to describe a source of real traffic. Topic Relevance: Although there have been various PLC networks related to work, had not seen a traffic model that allows to represent different kinds of service HAN networks. Aspects that can be considered useful in scenarios requiring evaluate aspects related to QoS (Quality of Service). Results: The proposed model allowed mathematically adequately represent different kinds of service, looking for the best estimate of the self-similar behavior present in the networks have and model the behavior that packets may occur within the PLC network under an innovative multi-multiclass stage. Application/Improvements: The proposed model can be used in practical and research scenarios as a strategy to evaluate the performance of networks and estimating the bandwidth required for various classes of service, in order to provide adequate levels of QoS during the provision of services such as voice, video, data and telemetry. It is suggested to evaluate other probability distributions, with the aim of identifying new strategies for modeling the self-similar traffic.
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  • Chee-Hock NG, Boon-Hee S. Queueing modelling fundamentals: With applications in communication networks. 2nd Ed. John Wiley and Sons; 2008. p. 1–294.

  • Vesga-Ferreira JC, Granados-Acuna G, Vesga-Barrera JA. Allocation of medium access order over Power Line Communications (PLC) supported on weighted voting games. Indian Journal of Science and Technology. 2017; 10(24):1–14. https://doi.org/10.17485/ijst/2017/v10i24/ 102667

  • Kleinrock L. Queueing Systems. Theory. Wiley, New York. 1975; 1:121–6.

  • Arun M, Reddy AAK, Thulasiram V. Optimal resource allocation and power management for mobile-to-mobile communication in celular network. Indian Journal of Science and Technology. 2015; 8(2):235–41. https://doi.org/10.17485/ijst/2015/v8iS2/62401

  • Bolch G, Greiner S, Meer H de, Trivedi KS. Queueing networks and Markov Chains: Modeling and performance evaluation with computer science applications. 2nd Ed.John Wiley and Sons. 2006. PMCid: PMC1557853. https://doi.org/10.1002/0471791571

  • Baskett F, Chandy K. Open, closed and mixed networks of queues with different clases of customers. Journal of the ACM. 1975; 22(2):248–60.

  • Pathan HB, Varma PS, Rajesh KS. QoS performanceof IEEE 802.11 in MAC and PHY layer using Enhanced OAR Algorithm. Indian Journal of Science and Technology. 2017; 10(9):1–9. https://doi.org/10.17485/ijst/2017/v10i9/98054

  • Leland WE, Taqqu MS, Willinger W, Wilson DV. On the self-similar nature of Ethernet traffic (extended version). IEEE/ACM Transactions on Networking. 1994; 2(1):1–15.

  • Neely MJ. Stochastic network optimization with application to communication and queueing systems. Morgan and Claypool Publishers; 2010. p. 1–211. PMid: 20061831.

  • Gebali F. Analysis of Computer and Communication Networks. Springer; 2008.

  • Mogollon JAM, Aguilar JJP, Ordo-ez VE, Montesino AFC. Caracterizacion y simulacion del trafico de redes LAN mediante el modelo MMPP. Revista Facultad de Ingeniería Universidad de Antioquia. 2007; 42:7–29.

  • Vesga JC, Sierra JE, Granados G. Evaluation of Models Hybrid (MH) and Smart (MS) based on game theory for resources allocation in HomeplugAV networks. Wseas Transactionson Communications. 2016; 15:1–17.

  • Sierra JE, Carlos Vesga J, Medina B. Livestock model innovation under the concept of smar tcities. Indian Journal of Science and Technology. 2018; 11(22):1–6. https://doi.org/10.17485/ijst/2018/v11i22/122510

  • FCC. Federal Communications Commission. 2018. https://www.fcc.gov/

  • Carlos Vesga FJ, Martha Fabiola Contreras H, Harold Esneider Perez W. Use of assignment models as a strategy for cannel optimization in the 5 GHz band supported in 802.11ac. Indian Journal of Science and Technology. 2018; 11(22):1–17. https://doi.org/10.17485/ijst/2018/v11i22/121348

  • Wittenberg N. UnderstandingVoiceover IP Technology. 1st Ed. New York, USA: Delmar Cengage Learning; 2009.

  • ITU-T (International Telecommunication Union). G.711: Pulse Code Modulation (PCM) of Voice Frequencies. ITU-T: Publications: Recommendations: G Series: G.711. 1988.

  • ITU-T (International Telecommunication Union). G.723.1 : Dual Rate Speech Coder for Multimedia Communications Transmitting at 5.3 and 6.3 Kbit/S.ITU-T: Publications: Recommendations: G Series: G.723.1. 2006.

  • ITU-T (International Telecommunication Union). G.729: Coding of Speech at 8 Kbits using ConjugateStructureAlgebraic-Code-Excited Linear-Prediction (CSACELP). ITU-T: Publications: Recommendations: G Series: G.729. 2007.

  • Tanenbaum A, Wheteral D. Computer Networks. 5th Ed. Boston, USA: Pearson-Prentice Hall; 2011.

  • Hsu J. Constrained simultaneous confidence intervals for multiple comparisons with the best. The Annals of Statistics. 1984; 12(3):1136–44.

  • Rivera DR, Granaiola F. Desarrollo y Despliegue de Servicios DVB-IP Con Software Open Source; Barcelona, Espa-a. 2010. p. 1–8.

  • Kikuchi Y, Nomura T, Fukunaga S, Matsui Y. RTP payload format for MPEG-4 audio/visual streams. Request for comments: 3016. Network Working Group; 2000. p. 1–21. PMid: 10838031. https://doi.org/10.17487/rfc3016

  • Meer JVD, Mackie D. RTP payload format for transport of MPEG-4 elementary streams. Request for comments: 3640. Network Working Group; 2003. p. 1–43.

  • Wenger S, Hannuksela M. RTP payload format for H. 264 Video. Request for comments: 3984. Network Working Group; 2005. p. 1–83.

  • Chen H, Bao C, Deng F, Zhang D, Jia M. A MDCT-based click noise reduction method for MPEG-4 AAC codec. International Conference on Wireless Communications and Signal Processing (WCSP). IEEE; 2011. p. 1–5.

  • Neogi A. Compression techniques for active video content. Proceedings DCC. Data Compression Conference; 2002. p. 1–466.

  • Withanage C, Ashok R, Yuen C, Otto K. A comparison of the popular home automation technologies. IEEE Innovative Smart Grid Technologies-Asia (ISGT ASIA); 2014. p. 600–5.


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  • Modeling and Characterization Traffic Voice, Video, Data and Telemetry under Pareto Distribution-Oriented Networks have on Power Line Communications

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Authors

Juan Carlos Vesga Ferreira
Escuela de Ciencias Basicas Tecnologia e Ingenieria (ECBTI), Universidad Nacional Abierta y a Distancia; Carrera 27 Nro. 40-43. Bucaramanga, Colombia
Martha Fabiola Contreras
Escuela de Ciencias Basicas Tecnologia e Ingenieria (ECBTI), Universidad Nacional Abierta y a Distancia; Carrera 27 Nro. 40-43. Bucaramanga, Colombia
Javier Emilio Sierra
Universidad de Sucre, Cra28 # 5-267, Sincelejo, Colombia

Abstract


Background/Objectives: To date, it has not found a traffic model that allows characterizing different kinds of service, which describe one self-similar behavior HAN (Home Area Network) networks on PLC (Power Line Communications). The aim of the article is to propose a model to characterize traffic classes such as voice, video, data and telemetry, describing a behavior autosimilar supported on the Pareto distribution. Methods: The Pareto distribution is a distribution type that is well aligned with autosimilar type of traffic. Pareto distribution is used as a source of network traffic, which has a bursty behavior, which can be repeated continuously within the network. To estimate the parameters describing the Pareto distribution for each class of service, a theoretical and experimental analysis was performed to determine the minimum rate, average and maximum, required to describe a source of real traffic. Topic Relevance: Although there have been various PLC networks related to work, had not seen a traffic model that allows to represent different kinds of service HAN networks. Aspects that can be considered useful in scenarios requiring evaluate aspects related to QoS (Quality of Service). Results: The proposed model allowed mathematically adequately represent different kinds of service, looking for the best estimate of the self-similar behavior present in the networks have and model the behavior that packets may occur within the PLC network under an innovative multi-multiclass stage. Application/Improvements: The proposed model can be used in practical and research scenarios as a strategy to evaluate the performance of networks and estimating the bandwidth required for various classes of service, in order to provide adequate levels of QoS during the provision of services such as voice, video, data and telemetry. It is suggested to evaluate other probability distributions, with the aim of identifying new strategies for modeling the self-similar traffic.

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DOI: https://doi.org/10.17485/ijst%2F2018%2Fv11i42%2F131033