Academic literature on the topic 'Models of self-similar and multifractal traffic'

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Journal articles on the topic "Models of self-similar and multifractal traffic"

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Millán, G., and G. Lefranc. "Development of Multifractal Models for Self-Similar Traffic Flows." IFAC Proceedings Volumes 46, no. 24 (September 2013): 114–17. http://dx.doi.org/10.3182/20130911-3-br-3021.00095.

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MONDRAGÓN C., RAÚL J. "A MODEL OF PACKET TRAFFIC USING A RANDOM WALL MODEL." International Journal of Bifurcation and Chaos 09, no. 07 (July 1999): 1381–92. http://dx.doi.org/10.1142/s021812749900095x.

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We consider the problem of modeling self-similar bursty packet traffic using chaotic maps. In particular, we introduce a family of maps to overcome the difficulties of describing the self-similar statistics of the packet traffic as a function of the maps' parameters. The packet traffic that can be modeled with these maps varies from Poisson to bursty traffic with different decay tails in the ON and OFF regions. In contrast with Fractional Brownian Models, these maps have a multifractal spectrum so they can generate packet traffic with the same mean, variance and self-similar parameter but different higher order statistics.
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Taqqu, Murad S., Vadim Teverovsky, and Walter Willinger. "Is Network Traffic Self-Similar or Multifractal?" Fractals 05, no. 01 (March 1997): 63–73. http://dx.doi.org/10.1142/s0218348x97000073.

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This paper addresses the question of whether self-similar processes are sufficient to model packet network traffic, or whether a broader class of multifractal processes is needed. By using the absolute moments of aggregate traffic measurements, we conclude that measured local-area network (LAN) and wide-area network (WAN) traffic traces, with the sample means subtracted, are well modeled by random processes that are either exactly or asymptotically self-similar.
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Radivilova, Tamara, Lyudmyla Kirichenko, Maksym Tawalbeh, Petro Zinchenko, and Vitalii Bulakh. "THE LOAD BALANCING OF SELF-SIMILAR TRAFFIC IN NETWORK INTRUSION DETECTION SYSTEMS." Cybersecurity: Education, Science, Technique 3, no. 7 (2020): 17–30. http://dx.doi.org/10.28925/2663-4023.2020.7.1730.

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The problem of load balancing in intrusion detection systems is considered in this paper. The analysis of existing problems of load balancing and modern methods of their solution are carried out. Types of intrusion detection systems and their description are given. A description of the intrusion detection system, its location, and the functioning of its elements in the computer system are provided. Comparative analysis of load balancing methods based on packet inspection and service time calculation is performed. An analysis of the causes of load imbalance in the intrusion detection system elements and the effects of load imbalance is also presented. A model of a network intrusion detection system based on packet signature analysis is presented. This paper describes the multifractal properties of traffic. Based on the analysis of intrusion detection systems, multifractal traffic properties and load balancing problem, the method of balancing is proposed, which is based on the funcsioning of the intrusion detection system elements and analysis of multifractal properties of incoming traffic. The proposed method takes into account the time of deep packet inspection required to compare a packet with signatures, which is calculated based on the calculation of the information flow multifractality degree. Load balancing rules are generated by the estimated average time of deep packet inspection and traffic multifractal parameters. This paper presents the simulation results of the proposed load balancing method compared to the standard method. It is shown that the load balancing method proposed in this paper provides for a uniform load distribution at the intrusion detection system elements. This allows for high speed and accuracy of intrusion detection with high-quality multifractal load balancing.
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Maulik, Krishanu, and Sidney Resnick. "The Self‐Similar and Multifractal Nature of a Network Traffic Model." Stochastic Models 19, no. 4 (November 2003): 549–77. http://dx.doi.org/10.1081/stm-120025404.

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Robert, Stephan, and Jean-Yves Le Boudec. "New models for pseudo self-similar traffic." Performance Evaluation 30, no. 1-2 (July 1997): 57–68. http://dx.doi.org/10.1016/s0166-5316(96)00055-7.

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Domański, Adam, Joanna Domańska, Katarzyna Filus, Jakub Szyguła, and Tadeusz Czachórski. "Self-Similar Markovian Sources." Applied Sciences 10, no. 11 (May 28, 2020): 3727. http://dx.doi.org/10.3390/app10113727.

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Markov queueing models are a powerful tool to evaluate the performance of computer networks and have been used in telecommunication studies for over 100 years. To apply them to the evaluation of the modern Internet, we should not only adapt them to the contemporary network structures but also include a description of the complex stochastic patterns (self-similarity and long-range dependance) of transmitted flows. We examine the features of two Markov models of an almost self-similar process, keeping in mind the modeling of Internet traffic. We have found that the obtained results are comparable with those achieved using a well-known generator of self-similar traffic.
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Millán, G., and G. Lefranc. "A Fast Multifractal Model for Self-Similar Traffic Flows in High-Speed Computer Networks." Procedia Computer Science 17 (2013): 420–25. http://dx.doi.org/10.1016/j.procs.2013.05.054.

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Al-Azzeh, Jamil Samih, Mazin Al Hadidi, R. Odarchenko, S. Gnatyuk, Z. Shevchuk, and Zhengbing Hu. "Analysis of Self-Similar Traffic Models in Computer Networks." International Review on Modelling and Simulations (IREMOS) 10, no. 5 (October 31, 2017): 328. http://dx.doi.org/10.15866/iremos.v10i5.12009.

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Aleksander, Marek, Roman Odarchenko, Sergiy Gnatyuk, and Tadeusz Kantor. "Basic characteristics of networks with self-similar traffic simulation." AUTOBUSY – Technika, Eksploatacja, Systemy Transportowe 20, no. 1-2 (February 28, 2019): 137–41. http://dx.doi.org/10.24136/atest.2019.023.

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This paper is devoted to simulations the networks with self-similar traffic. The self-similarity in the stochastic process is identified by calculation of the Herst parameter value. Based on the results, received from the experimental research of network performance, we may conclude that the observed traffic in real-time mode is self-similar by its nature. Given results may be used for the further investigation of network traffic and work on the existing models of network traffic (particularly for new networks concepts like IoT, WSN, BYOD etc) from viewpoint of its cybersecurity. Furthermore, the adequacy of the description of real is achieved by complexifying the models, combining several models and integration of new parameters. Accordingly, for more complex models, there are higher computing abilities needed or longer time for the generation of traffic realization..
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Dissertations / Theses on the topic "Models of self-similar and multifractal traffic"

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Kirichenko, L., I. Ivanisenko, and T. Radivilova. "Investigation of Multifractal Properties of Additive Data Stream." Thesis, 1 th IEEE International Conference on Data Stream Mining & Processing, 2016. http://openarchive.nure.ua/handle/document/3810.

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The work presents results of a numerical study of fractal characteristics of multifractal stream at addition of stream, which does not have multifractal properties. They showed that the generalized Hurst exponent of total stream tends to one of original multifractal stream with increase in signal/noise ratio.
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Takada, Hellinton Hatsuo. "Design of high-speed networks considering monofractal and multifractal traffic models." Instituto Tecnológico de Aeronáutica, 2007. http://www.bd.bibl.ita.br/tde_busca/arquivo.php?codArquivo=425.

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Fractal traffic models capture uniquely statistical scaling characteristics and the burstiness of the real network traffic. The impact of such traffic models on existing networks has been studied extensively in the related literature and the relevance of such analysis to dimension the necessary resources and evaluate correctly the performance of these systems using computational simulation is well-known. On the other hand, the project of network algorithms already taking into account these models possesses several open issues due to the constant evolving network technologies. The design of high-speed networks considering fractal traffic models is the objective of this work and in order to illustrate the extension of the universe of possible applications, several different network scenarios were chosen: traffic monitoring applied to an intrusion detection system, traffic regulation to maintain the necessary quality of service in a domain according to some service level agreement, arriving traffic conformation at the entrance of an optical network domain and the planning of the necessary bandwidth for a satellite-based network carrying Internet traffic. In this work, important contributions were obtained for the previous enumerated scenarios: statistical process controls to monitor fractal sequences were developed; the leaky bucket, a traditional traffic regulator, was extended to be able to deal with fractal traffic; approaches to plan burst assembly algorithms, a class of traffic shaping mechanisms of optical burst switching networks, were introduced for the case when the incoming traffic is fractal and an estimative of the necessary bandwidth of a satellite-based demand-assignment multiple access system receiving fractal traffic at its entrance was obtained. All the presented results were theoretically and numerically studied. The results show the superior performance of the proposed solutions compared with existent techniques when the local traffic is fractal.
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Sadek, Mohamed Nayera Mahmoud. "High-speed network traffic prediction and its applications using neural networks and self-similar models." 2004. http://gateway.proquest.com/openurl?url%5Fver=Z39.88-2004&rft%5Fval%5Ffmt=info:ofi/fmt:kev:mtx:dissertation&res%5Fdat=xri:pqdiss&rft%5Fdat=xri:pqdiss:3131531.

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Thesis (Ph.D. in Electrical Engineering)--S.M.U.
Source: Dissertation Abstracts International, Volume: 65-05, Section: B, page: 2558. Adviser: Alireza Khotanzad. Includes bibliographical references.
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Book chapters on the topic "Models of self-similar and multifractal traffic"

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Bragg, Arnold W. "Simulation with Self-Similar Traffic Models." In The Kluwer International Series in Engineering and Computer Science, 105–33. Boston, MA: Springer US, 2002. http://dx.doi.org/10.1007/978-1-4615-0863-2_6.

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Liu, Hai, Nirwan Ansari, and Yun Q. Shi. "MPEG Video Traffic Models: Sequentially Modulated Self-Similar Processes." In Broadband Communications, 63–72. Boston, MA: Springer US, 2000. http://dx.doi.org/10.1007/978-0-387-35579-5_6.

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Girão-Silva, Rita, and José Craveirinha. "A Comparative Study on Simulation Models for Self-similar Traffic." In Lecture Notes in Computer Science, 571–80. Berlin, Heidelberg: Springer Berlin Heidelberg, 2003. http://dx.doi.org/10.1007/978-3-540-45076-4_57.

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Watkins, N. W., B. Hnat, and S. C. Chapman. "On Self-Similar and Multifractal Models for the Scaling of Extreme Bursty Fluctuations in Space Plasmas." In Extreme Events and Natural Hazards: The Complexity Perspective, 299–313. Washington, D. C.: American Geophysical Union, 2012. http://dx.doi.org/10.1029/2011gm001084.

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Radev, Dimitar, Izabella Lokshina, and Svetla Radeva. "Modeling and Simulation of Self-Similar Traffic in Wireless IP Networks." In Networking and Telecommunications, 1631–47. IGI Global, 2010. http://dx.doi.org/10.4018/978-1-60566-986-1.ch105.

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The article examines self-similar properties of real telecommunications network traffic data over a wide range of time scales. These self-similar properties are very different from the properties of traditional models based on Poisson and Markov-modulated Poisson processes. Simulation with stochastic and long range dependent traffic source models is performed, and the algorithms for buffer overflow simulation for finite buffer single server model under self-similar traffic load SSM/M/1/B are explained. The algorithms for modeling fixedlength sequence generators that are used to simulate self-similar behavior of wireless IP network traffic are developed and applied. Numerical examples are provided, and simulation results are analyzed.
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Nogueira, Antóniol, Paulo Salvador, Rui Valadas, and António Pacheco. "Modeling IP Traffic Behavior through Markovian Models." In Encyclopedia of Internet Technologies and Applications, 305–15. IGI Global, 2008. http://dx.doi.org/10.4018/978-1-59140-993-9.ch044.

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This article addresses the use of Markovian models, based on discrete time MMPPs (dMMPPs), for modeling IP traffic. In order to describe the packet arrival process, we will present three traffic models that were designed to capture self-similar behavior over multiple time scales. The first model is based on a parameter fitting procedure that matches both the autocovariance and marginal distribution of the counting process (Salvador 2003). The dMMPP is constructed as a superposition of two-state dMMPPs (2-dMMPPs), designed to match the autocovariance function, and one designed to match the marginal distribution. The second model is a superposition of MMPPs, each one describing a different time scale (Nogueira 2003a). The third model is obtained as the equivalent to a hierarchical construction process that, starting at the coarsest time scale, successively decomposes MMPP states into new MMPPs to incorporate the characteristics offered by finer time scales (Nogueira 2003b). These two models are constructed by fitting the distribution of packet counts in a given number of time scales.
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Conference papers on the topic "Models of self-similar and multifractal traffic"

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Da Silva, Maykon Renan Pereira, and Flávio Rocha. "Método de Estimação de Parâmetros para Modelagem no Domínio Wavelet do Tráfego de Redes de Computadores Usando o Algoritmo de Levenberg-Marquardt." In Workshop em Desempenho de Sistemas Computacionais e de Comunicação. Sociedade Brasileira de Computação - SBC, 2020. http://dx.doi.org/10.5753/wperformance.2020.11104.

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Research has shown that analysis and modeling techniques that provide a better understanding of the behavior of network traffic flows are very important in the design and optimization of communication networks. For this reason, this work proposes a multifractal model based on a multiplicative cascade in the wavelet domain, to synthesize network traffic samples. For this purpose, in the proposed model, a parametric modeling based on an exponential function is used for the variance of the multipliers along the stages of the cascade. The exponential function parameters are obtained through the solution of a non-linear system, for this purpose, the Levenberg-Marquardt method is used. The main contribution of the proposed algorithm is to use a fixed and reduced number of parameters to generate network traffic samples that have characteristics such as self-similarity and wide Multifractal Spectrum Width (MSW) similar to the real network traffic traces and without the need for prior adjustment of these parameters.
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Rao, Raghuveer M., S. Lee, Soheil A. Dianat, and Athimootil V. Mathew. "Linear scale-invariant system models for self-similar wireless traffic characterization." In AeroSense 2000, edited by Raghuveer M. Rao, Soheil A. Dianat, and Michael D. Zoltowski. SPIE, 2000. http://dx.doi.org/10.1117/12.394095.

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Gonzalez, Jorge, Jorge Gonzalez, and Chad A. Bollmann. "Aggregated Impulses: Towards Explanatory Models for Self-Similar Alpha Stable Network Traffic." In 2019 13th International Conference on Signal Processing and Communication Systems (ICSPCS). IEEE, 2019. http://dx.doi.org/10.1109/icspcs47537.2019.9008701.

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Lee, Seungsin, Raghuveer M. Rao, and Rajesh Narasimha. "Models for discrete-time self-similar vector processes with application to network traffic." In AeroSense 2003, edited by Raghuveer M. Rao, Soheil A. Dianat, and Michael D. Zoltowski. SPIE, 2003. http://dx.doi.org/10.1117/12.498577.

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Elagha, Humam, and Maher AlShafee. "On the Self-Similar Nature of ATM Network Traffic." In InSITE 2007: Informing Science + IT Education Conference. Informing Science Institute, 2007. http://dx.doi.org/10.28945/3145.

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Modeling multimedia traffic is an important issue in performance analysis and design of communication networks. With introduction of new applications, the characteristics of data traffic changes. In this paper, a measurement study of ATM Network traffic has been carried out and it is shown that the recorded data exhibit self-similar features. The conclusions are supported by a comprehensive analysis using one of the most popular statistical methods called Indices of Dispersion. Our results validate one of the most striking findings of the present teletraffic research: a broad range of packet network traffic has fractal-like behavior. We also investigate three popular synthetic self-similar traffic models and find out the most accurate one for the measured traffic.
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Schwefel, Hans Peter, and Lester Lipsky. "Impact of aggregated self-similar on/off traffic on delay in stationary queueing models." In Photonics East '99, edited by Robert D. van der Mei and Daniel P. Heyman. SPIE, 1999. http://dx.doi.org/10.1117/12.360370.

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