Development of a multi-mode self-adaptive algorithm to create an efficient wireless network on a university campus

The expanding use of ubiquitous computing has created a significant demand on existing network infrastructures. The demands of voice, video, and data on the same medium require a quality of service (QoS) at a level acceptable to users. Many network providers simply scale their networks to increase bandwidth and hardware to deal with the increasing demands. However, a network may still reach its design limits with peak traffic or malicious overuse of resources. In addition, with technology changing at a rapid pace, it is difficult to provide sufficient staffing to monitor and adjust the network settings to avoid issues during periods of network saturation. One of the common method to address these issues involves implementing a traffic shaper. A traffic shaper is a computer network management technique by which data sent across the network is delayed or routed in a way to accommodate a specific level of traffic to reach a desired QoS. There are many existing traffic shaping algorithms, each performing well under specific circumstances improving some QoS measures. The algorithms make use of queuing schemes to sort and send traffic based on the parameters provided to the system. To determine the need for this research, a survey was administered which revealed dissatisfaction with QoS of the wireless network. The purpose of this study focused on the development of a traffic shaping algorithm that would improve the QoS on a local area network on a university campus. The goal of the research was to create a new architecture that would allow a router to dynamically shift between different queuing mechanisms to improve network delay and packet loss without negatively impacting data throughput. The Multi-Mode Self-Adaptive (MMSA) algorithm was proposed to define a mechanism for this architecture. The MMSA was implemented within the code of a Cisco® router in the OPNET Modeler software and tested in a simulated university network environment. The results of the simulation revealed an improvement in end to end delay and packet loss rate with an insignificant change in average transmit rate between the router and the external server. The results of this research can be used as a basis for future research to create a new QoS framework. The new framework could be implemented in a router to allow configurations tailored to the network requirements of a service provider

Accurate Heavy Tail Distribution Approximation For Multifractal Network Traffic

In this paper, we propose the use of a Gaussian mixture model to represent the heavy tail distribution of modern network traffic traces. Another novel contribution of this work is the derivation of a general expression for loss probability estimation in a single server queueing system for traffic traces with multifractal characteristics. The efficiency of this statistical modeling and the accuracy of the estimated loss probabilities are experimentally validated by comparing with other four multifractal based approaches: two of them considering two specific heavy tail distributions (lognormal, Pareto) and the well-known MSQ (Multiscale Queue) and CDTSQ (Critical Dyadic Time-Scale Queue) methods.45317Leland, W., Taqqu, M., Willinger, W., Wilson, D., On The Self-Similar Nature of Ethernet Traffic (1994) IEEE/ACM Transactions on Networking, 2 (1), pp. 1-15. , extended version FebNorros, I., A Storage Model with Self-Similar Input (1994) Queueing, 16, pp. 387-396Park, K., Willinger, W., (2000) Self-Similar Network Traffic and Performance Evaluation, , John Wiley and Sons New YorkRiedi, R.H., Crouse, M.S., Ribeiro, V.J., Baraniuk, R.G., A Multifractal Wavelet Model with Application to Network Traffic (1999) IEEE Transactions on Information Theory. (Special Issue on Multiscale Signal Analysis and Modeling), 45, pp. 992-1018. , AprilVieira, F.H.T., Lee, L.L., Adaptive Wavelet Based Multifractal Model Applied to the Effective Bandwidth Estimation of Network Traffic Flows (2009) IET Communications, pp. 906-919. , JuneKrishna, P.M., Gadre, V.M., Desai, U.B., (2003) Multifractal Based Network Traffic Modeling, , Kluwer Academic Publishers, Boston, MAPeltier, R., Véhel, J.L., (1995) Multifractional Brownian Motion: Definition and Preliminary Results, , Technical Report 2695, INRIAVieira, F.H.T., Bianchi, G.R., Lee, L.L., A Network Traffic Prediction Approach Based on Multifractal Modeling (2010) J. High Speed Netw, 17 (2), pp. 83-96McLachlan, G., (1988) Mixture Models, , Marcel Dekker, New York, NYMartinez, W.L., Martinez, A.R., (2008) Computational Statistics Handbook with Matlab, , Chapman & Hall/CRC, Boca Raton, FloridaFisher, A., Calvet, L., Mandelbrot, B.B., (1997) Multifractality of Deutschmark/US Dollar Exchanges Rates, , Yale UniversitySeuret, S., Gilbert, A.C., Pointwise Hölder Exponent Estimation in Data Network Traffic ITC Specialist Semina, Monterey, 2000Stenico, J.W.G., Lee, L.L., Modelagem de Processos Multifractais Baseada em uma Nova Cascata Conservativa Multiplicativa (2011) XXIX Simpósio Brasileiro de Telecomunicações - SBRT 11, 1, pp. 1-6. , 10/2011, Curitiba, PR, BrasilStenico, J.W.G., Lee, L.L., A New Binomial Conservative Multiplicative Cascade Approach for Network Traffic Modeling 27th IEEE International Conference on Advanced Information Networking and Applications - IEEE AINA 2013Falconer, K., (2003) Fractal Geometry: Mathematical Foundations and Applications, , Second Edition Wiley2 Edition November 17Riedi, R.H., An improved multifractal formalism and self-similar measures (1995) Journal of Mathematical Analysis and Applications, 189, pp. 462-1190Asmussen, S., (2000) Ruin Probabilities, , World Sicientific, SingapuraBenes, V., (1963) General Stochastic Processes in Theory of Queues, , Reading, MA: Addison WesleyStenico, J.W.G., Ling, L.L., A Multifractal Based Dynamic Bandwidth Allocation Approach for Network Traffic Flows IEEE International Conference on Communications (ICC), 23-27 May 2010, pp. 1-6Stenico, J.W.G., Ling, L.L., A Control Admission Scheme for Pareto Arrivals with Multi-Scale Characteristics Proceedings of the International Workshop on Telecommunications - IWT 2011, pp. 220-224. , May - 2011, Rio de Janeiro - BrazilRibeiro, V.J., Riedi, R.H., Crouse, M.S., Baraniuk, R.G., Multiscale Queueing Analysis of Long-Range-Dependent Network Traffic IEEE INFOCOM 2000, pp. 1026-1035. , Tel Aviv, Israelhttp://ita.ee.lbl.gov/html/traces.htmlhttp://www.cs.columbia.edu/~hgs/internet/traces.htmlhttp://crawdad.cs.dartmouth.edu/umd/sigcomm200

Queuing Modeling Applied To Admission Control Of Network Traffic Flows Considering Multifractal Characteristics

In this paper, we propose an analytical expression for estimating the byte loss probability at a single server queue with multifractal traffic arrivals. Initially we address the theory concerning multifractal processes, especially the Hölder exponents of the multifractal traffic traces. Next, we focus our attention on the second order statistics for multifractal traffic processes. More specifically, we assume that an exponential model is adequate for representing the variance of the traffic process under different time scale aggregation. Then, we compare the performance of the proposed approach with some other relevant approaches. In addition, based on the results of the analysis, we propose a new admission control strategy that takes into account the multifractal traffic characteristics. We compare the proposed admission control strategy with some other widely used admission control methods. The simulation results show that the proposed loss probability estimation method is accurate, and the proposed admission control strategy is robust and efficient. © 2003-2012 IEEE.112749758Perlingeiro, F., Lee, L.L., A new bandwidth estimation approach for fractal processes (2005) IEEE Latin America Transactions, 3 (5), pp. 436-446. , DecemberStênico, J.W.G., Lee, L.L., A new binomial conservative multiplicative cascade approach for network traffic modeling (2013) 27th IEEE International Conference on Advanced Information Networking and Applications-IEEE AINA 2013, 1, pp. 794-801. , Mach 25-28 Barcelona, SpainLeland, W.E., Taqqu, M.S., Willinger, W., Wilson, D.V., On the self-similar nature of ethernet traffic (extended version) (1994) IEEE/ACM Transactions on NetworkingWillinger, W., Taqqu, M.S., Erramilli, A., (1996) A Bibliographical Guide to Self-similar Traffic and Performance Modeling for Modern HighSpeed Stochastic Networks: Theory and Applications", 4. , Royal Statistical Society Lecture Notes Series Oxford University PressPark, K., Willinger, W., (2000) Self-Similar Network Traffic and Performance Evaluation", , New York: WileyVieira, F.H.T., Bianchi, G.R., Lee, L.L., A network traffic prediction approach based on multifractal modeling (2010) J. 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