Abstract
This paper describes modeling of the available bit rate (ABR) source traffic in asynchronous transfer mode (ATM) network using BLPos/GTEXP traffic generator, which employs Poisson distribution for modeling the burst length (BLPos) and exponential distribution for modeling the gap time (GTEXP). This traffic generator inherits the advantages of both Poisson and exponential distribution functions to achieve enhanced link performance. Analytical and simulation results for BLPos/GTEXP traffic generator have been presented and compared.
Highlights
The Poisson process is an extremely useful process for modeling purposes in many practical applications, such as, for example, to model arrival processes for queueing models or demand processes for inventory systems
The performance of the relative rate marking (RRM) switch was evaluated for traffic generator with respect to the allowed cell rate (ACR), switch input rate (SWIR)/switch output rate (SWOR), memory access time (MAT), queue length (Q), and cell transfer delay (CTD)
The initial value of ACR for sources Si was taken as peak cell rate (PCR)/2 whereas the final ACR value was kept between 200 to 700 cells/sec in incremental steps of 100 for i = 1, 2, . . . 6 and taking buffer size = 1000 cells, higher queue threshold (QH ) = 200 cells, lower queue threshold (QL) = 100 cells, and assuming that each source has to send a total of 1000 cells
Summary
The Poisson process is an extremely useful process for modeling purposes in many practical applications, such as, for example, to model arrival processes for queueing models or demand processes for inventory systems. In this paper source traffic modeling and simulation have been carried out for ABR service in ATM networks using Poisson distribution for modeling the burst length (BLPos) and exponential distribution for modeling the gap time (GTExp). Bearing in mind that λExp = ACR, since in ABR service a source sends its data with a rate equal to ACR, defining λGTExp as the exponential mean arrival rate for GTExp, and considering (14) in terms of time, we get (20). The source first starts transmitting a random-sized burst of cell at ICR It waits for a random amount of time, which follows exponential distribution. The source repeats the calculation of Poisson distribution, (4) with μPos = ACR, k times, where k is changing from 0 to peak cell rate (PCR).
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