International Journal of Engineering Research in Computer Science and Engineering (IJERCSE)

Analysis Self Similarity Traffic in Next Generation Networks

Author : Liji P ¹ Dr.S.Bose ²

Date of Publication :7th September 2016

Abstract: Ubiquitous IP based Next Generation Networks characterized by seamless mobility. According to the Received Signal Strength (RSS) mobile terminal in the network will be roaming in the vicinity of the heterogeneous wireless network and have frequent handover from one technology\ to another. As there is frequent handoff from one technology to another,the performance of the mobile terminal in the network will degrade bue to non-availability resources. Moreover network infrastructure has to handle a huge amount of IP traffic, including significant realtime traffic with guarantied Quality of Service (QoS).The stringent Quality of Service (QoS) parameters like delay, delay variance and packet loss will also be affected. Traffic models is a mathematical approximation for real traffic behavior can account traffic in the network and this can be used as input to analysis resource allocation strategies, reduce end to end delay, packet loss and jitters in the NGN environment to meet the QoS given by the Service Level Agreement(SLA).Real time network traffic is complex, as it exhibits strong dependencies. Self-similarity with high variability and therefore classical models of time series such as Poisson and Markov processes are not appropriate for modeling. These models will underestimate the burrstones of traffic. Self similarity models like Fractional Gaussian Noise(FGN),Fractional Brownian Motion (fBM),Fractional-ARIMA and M=G=1 can represent the high variability in the traffic. From these model FARIMA will best fit for the high priority real time VBR traffic. FARIMA can represent the coexistence of SRD and LRD along with stability innovation. Future traffic in the network can be predicted from present and past history. According to predicted value, resource mainly bandwidth can dynamically allocated on demand.

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