COMBINING THE BURST AND CELL SCALES The finite-capacity buffer is a fundamental element of ATM where cells multiplexed from a number of different input streams are temporarily stored awaiting onward transmission. The flow of cells from the different inputs, the number of inputs, and the rate at which cells are served determine the occupancy of the buffer and hence the cell delay and cell loss experienced. So, how large should this finite buffer be? In Chapters 8 and 9 we have seen that there are two elements of queueing behaviour: the cell-scale and burst-scale components. . | Introduction to IP and ATM Design Performance With Applications Analysis Software Second Edition. J M Pitts J A Schormans Copyright 2000 John Wiley Sons Ltd ISBNs 0-471-49187-X Hardback 0-470-84166-4 Electronic 12 Dimensioning real networks don t lose cells COMBINING THE BURST AND CELL SCALES The finite-capacity buffer is a fundamental element of ATM where cells multiplexed from a number of different input streams are temporarily stored awaiting onward transmission. The flow of cells from the different inputs the number of inputs and the rate at which cells are served determine the occupancy of the buffer and hence the cell delay and cell loss experienced. So how large should this finite buffer be In Chapters 8 and 9 we have seen that there are two elements of queueing behaviour the cell-scale and burst-scale components. We evaluated the loss from a finite buffer for constant bit-rate variable bit-rate and random traffic sources. For random traffic or for a mix of CBR traffic only the cell-scale component is present. But when the traffic mix includes bursty sources such that combinations of the active states can exceed the cell slot rate then both components of queueing are present. Let s look at each type of traffic and see how the loss varies with the buffer size for different offered loads. We can then develop strategies for buffer dimensioning based on an understanding of this behaviour. First we consider VBR traffic this combines the cell-scale component of queueing with both the loss and delay factors of the burst-scale component of queueing. Figure shows how the burst-scale loss factor varies with the number of sources N where each source has a peak cell rate of 24000 cell s and a mean cell rate of 2000cell s. From Table we find that the minimum number of these sources required for burst-scale queueing is N0 . Table gives the burst-scale loss factor CLPbsl at three different values of N 30 60 and 90 sources as well as the offered load as a
