trajjic and to manage large span of resources and services effectively. .... 51-62 vol. 1. [5] C. Khunbo and S. Banerjee, âNetwork traffic characterization.
Network Traffic Characterization for High-speed Networks Supporting Multimedia Khaled M. Elleithy
Ali S. Al-Suwaiyan
Computer Science and Engineering Department University of Bridgeport Bridgeport, CT 06601
Computer Engineering Department King Fahd University Dhahran 31261, Saudi Arabia
clleithy @ bridgcport.cdu
data source (e.g., video, voice, multimedia, ...). Then the sample is studied well to see which distribution typifies its first and second order statistics. When a distribution is found, we must calculate the best fitting curve by choosing the distribution parameters carefully. After that, the optimal distribution is an accurate characterization of the traffic. Usually, secondary steps come after that which are dependent on the characteristics of the traffic, such as simulation. In this paper, we have conducted a simulation study to get the steady-state probabilities. The process of traffic characterization needs an empirical study of the source traffic to determine its distribution [ 1-6,10,13- 141. W e have assumed the distribution of the traffic source and we have studied the effects of this assumption by measuring the steady-state probabilities assuming a G/D/l system with infinite buffer. The result of this study can help in determining the needed buffer size and several useful metrics (e.g., throughput, response time, . .. etc). This paper is organized as follows. The problem under study is defined in the next section. Then, we show simulation results after describing the simulation mechanism. Finally, the paper offers conclusions.
Abstract Continuously growing needs for distributed applications that transmit massive amount of data has led to the emergence of high-speed networks that require broadband and multimedia capabilities. Such networks are supposed to have the ability to handle heterogeneous trajjic and to manage large span of resources and services effectively. In this paper, a single server G/D/l queuing system with infinite buffer is simulated with the consideration of three input traffic sources: exponential, weibull, and normal distrbutions. The upper bounds on buffer size are evaluated for the given distributions.
1. Introduction Without the knowledge of traffic characteristics, we would not meet what networks are supposed to achieve. For example, without accurate traffic characterization, the network may be forced to use overly conservative resource schemes leading to underutilized servers. Traffic characterization is an integral part of queuing systems employed in the study of network, protocol, and switch design performance. It serves the following purposes [ I 21: Helps in specifying critical QoS parameters such as buffer size and link capacity. Predicting bandwidth requirements which allows for better capacity assignment and congestion control in communication networks Estimation of statistical multiplexing gains of VBR transmission over B-ISDNs Mathematical analysis and simulation of traffic signals models in the process of designing communication networks.
2. Problem Definition W e have a single server model of type G/D/l with infinite buffer, where G (input distribution) can be one of the following distributions: 1 )Exponential 2)Weibull 3)Normal These models are used with a single server queue, which can be considered as a router with infinite buffer as shown in Figure 1. These input models are evaluated one at a time independently, not all together, and for each input model, we approximated the steady-state probabilities. The objective is to approximate the steady-state ._ probabilities using simulation, given the above input distribution, one at a time.
In general, traffic Characterization goes through the following steps. First, a traffic sample is generated from a
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1 1 Exponential], I
infinite Queue I ’
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Figure 1. System Model b)
3. Simulation Model c) In this section, we develop a discrete event simulator that simulates the behavior of the mentioned single server G/D/l queuing system [15]. We first describe the simulation mechanism used in the simulator, and then we give some simulation results.
Weibull: this routine is used to generate weibull distributed interarrival times. Normal: this routine is used to generate normally distributed interarrival times.
P ( i ) = Cl ~
2Ck k=O
3.1. Simulation Mechanism
In order to approximate the steady-state probabilities P(i), we have used the following formula:
We have used an event-driven simulator programmed using the C language. The simulator is divided into the following components:
Where C, is the number of times in which the system has i customers and m represents the maximum number of customers that the system has got during the simulation period. For the purpose of approximation, we can neglect P(n) for n > m.
External definitions: this part includes the “#include” directives, the “#define” directives, the global variable declarations and functions prototypes. Main function: this part controls the overall behavior of the simulator. Initialization routine: in this routine, we initialize some variables including simulation clock, state variables, the event list ... etc. Timing routine: this routine determines the type of the next event and advances the simulation clock. New arrival routine: this is executed whenever we have an arrival event. It schedules next customer arrival and current customer departure times. Departure routine: this is executed whenever we have a departure event. If there is a customer waiting in the queue, the routine will schedule its departure. Statistics calculation routine: This is a routine to calculate some statistics, e.g., the frequency of having i customers in the system, maximum number of customers during the simulation period ... etc. Random variate generator: this could be one of the following depending on the input distribution: a) Expon: this routine is used to generate exponentially distributed interarrival times.
3.2. Simulation Results In this section we present some experimental results. For each input distribution, we have run three experiments with different parameters, but all the experiments have the same service time, which is fixed at 0.5 time units. We have fixed the service time to isolate its effect and see only the effects of changing the input distribution. Figures 2-10 Show charts of P(N), which are the steady-state probabilities, versus N , which represents the number of customers in the system. These charts give us hints about the minimum amount of buffer needed to handle customers in the queue. For example, Figure2 shows us that we should have at least a buffer of size seven, because, as seen from the figure, the probability of having above seven customers in the system can be neglected. Table 1 shows upper bounds on buffer size associated with each input traffic type.
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[6] W. Bin and A. Gupta, “Traffic characterization of VBR video streams using uniform transmission in ATM networks” Proc. of I997 international con5 on ICICS., 1997, pp. 16471651 vol. 3. [7] B. Mark, D. Jagerman, and G. Ramamurthy, “Peakedness measures for traffic characterization in high-speed networks” IEEE INFOCOM’97, 1997, pp. 427-435 v01.2. [8] C. Wu, J. Jiau, and K. Chen, “Characterizing traffic behavior and providing end-to-end service guarantees within ATM networks” IEEE INFOCOM’97, 1997, pp. 336-344 vol. 1. [9] W. Smith and A. Zaghloul, “Network traffic characterization in a transaction based modeling environment” Zfld IEEE Symposium on Computers and Communications, 1997, pp.28-33. [IO] Y. Lin, T. Huang, and Y. Lai, “Characterization and control of highly correlated traffic in high speed networks”, Proc. of 21” IEEE con5 on Local Computer Networks, 1996, pp. 1927. [ I I ] E. Knightly, R. Mines, and H. Zhang, “Deterministic characterization and network utilization for several distributed real-time applications” First workshop on Object-Oriented Real-Time Dependable Systems, 1995, pp. 63-70. [ 121 T. Ndousse, “Fuzzy characterization and control of QoS in multimedia cell-based networks” IEEE GLOBECOM’97, 1997, pp.1662-1667 ~01.2. [I31 C. Bisdikian, E. Spiegel, and A. Tantawi, “Traffic characterization on high-speed Token-Ring networks” IEEE workshop on the HPCS’92, 1992, pp. 1-4. [ 141 D. Wrege and J. Liebherr, “Video traffic characterization for multimedia networks within a deterministic service” IEEE INFOCOM’96, 1996, pp.537-544 ~01.2. [I51 A. M. Law and W. D. Kelton, Simulation Modeling & Analysis, 2”d edition, McGrawHill, 1991.
4. Conclusion Traffic modeling or characterization describes the random flow of traffic associated with network sources in terms of stochastic models. Network sources might be a VBR (Variable Bit-Rate) video or L A N W A N data. In this paper, it was shown how traffic characterization helped in determining critical QoS parameters, such as buffer size. In addition, we have seen how the upper bound on buffer changes as the input distribution changes. These results on the importance of traffic characterization as a necessary step on evaluating the performance of a network system that supports multimedia applications.
5. References [ I ] T. Taralp, M. Devetsikiotis, and I. Lambadaris, “Traffic characterization for QoS provisioning in high-speed networks” Proc. of the 31”’ Hawaii international col$ on System Sciences, 1998, pp. 485-492 vol. 7. [2] C. Nayak and M. Ilyas, “Characterization of video conferencing traffic in ATM networks” Proc. of the IEEE, Southeastcon ‘96, 1996, pp.344-347. [3] A. Tarraf, I. Habib, and T. Saadawi, “Characterization of packetized voice traffic in ATM networks using neural networks” IEEE GLOBECOM’93, 1993, pp. 996-1000 v01.2. [4] P.P. Tang and T. Tai, “Network traffic characterization using token bucket model” IEEE INFOCOM’99, 1999, pp. 51-62 vol. 1. [ 5 ] C. Khunbo and S. Banerjee, “Network traffic characterization of distributed database applications” Proc. Of the 31“‘ Annual Simulation Symposium, 1998, pp. 98-105.
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