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Following the simple service model introduced in Section 16.5, we assume throughput quality of service is satis ed by limiting the number of elastic ows on a link and seek to dimension link capacity such that the blocking probability is less than some low target value E. Consider rst an isolated link handling only elastic ows. Assuming Poisson arrivals, a minimum throughput requirement y, exact fair shares (i.e., processor sharing service), and a link bandwidth of c ny, the probability of blocking is equal to the saturation probability in an M =G=1 processor sharing queue of capacity n: Be rn 1 r = 1 rn 1 where r is the link load. 16:2

DECLARE EXTENSION LDD and DECLARE EXTENSION PDD Although device drivers are dynamically loadable DLLs, there may be some instances where a device driver must perform some one-off initialization at boot time. For example, media drivers register themselves with the local media sub-system at boot time to provide information about the number of supported drives, partitions and drive numbers prior to the driver being loaded (this is described in detail in 13, Peripheral Support ). To support this, you should use the DECLARE_STANDARD_EXTENSION macro previously described, in conjunction with the following macros to export the required factory function:

Since elastic ows use bandwidth more ef ciently, blocking probability (16.2) can be considerably less than the corresponding probability for stream traf c requiring constant rate y, as given by Erlang's formula E nr; n . The line labeled ``elastic'' in Fig. 16.3 shows achievable utilization r for elastic traf c such that Be , given by Eq. (16.2), is equal to 0.01. These results clearly illustrate the scale economies effect and the greater ef ciency of elastic sharing. The advantage of elastic sharing with respect to rigid rate allocations is somewhat mitigated in a network where ows cannot always attain a full share of available link bandwidth because of congestion on other links of their path and their own limited peak rate. If, however, the ows can at least attain rate y and this rate is guaranteed by admission control on every network link, the utilization predicted by the Erlang formula constitutes a lower bound. In other words, the Erlang formula can be used as a conservative dimensioning tool to determine the traf c capacity of a link dedicated to elastic traf c: a link of capacity c can handle a volume of elastic traf c Ae ( ow arrival rate average size) with minimum throughput y and blocking probability less than E if E Ae ; c=y E. Given the scale economies achieved with the Erlang formula, this simple dimensioning approach is ef cient if c=y is large (e.g., Ae =c > 0:8 if c=y > 100 for a 1% blocking probability). An advantage of the above approach is that the integration of stream and elastic traf c is taken into account simply by including the latter as an additional traf c class in the multirate dimensioning methods alluded to in the previous section. 16.7 CONCLUSION

abstract class JTree extends FJTask { volatile double maxDiff; // for convergence check } class Interior extends JTree {

#define DECLARE_EXTENSION_LDD() \ EXPORT_C DLogicalDevice* CreateLogicalDevice() #define DECLARE_EXTENSION_PDD() \ EXPORT_C DPhysicalDevice* CreatePhysicalDevice()

The realization of quality of service guarantees in a multiservice network depends more on sound traf c engineering than on the de nition of a service model allowing priority access for an unde ned number of privileged users. We have argued that the service model should facilitate traf c engineering by distinguishing two broad categories of traf c: stream and elastic. For each category, the appropriate entity for traf c management is an individual ow (e.g., one videoconference, one le transfer) and not either an isolated packet or some aggregation of ows. A tentative simple service model is based on just two traf c classes. One class destined for stream traf c is based on open-loop control and uses ``bufferless multiplexing'' with measurement-based admission control. This choice enables delay and loss rate performance guarantees, even for self-similar ows. The leaky bucket is not useful as a traf c descriptor and the only traf c parameter required here is the ow peak rate. The second service class uses closed-loop control to share bandwidth between elastic ows. We advocate a lightweight form of admission control for elastic traf c, requiring that each link identify the ows it is currently transporting. Per ow queueing would be useful to enforce fairness, or to share bandwidth more ef ciently by giving priority to short transfers, for example. In the simple bandwidth sharing models considered here, the heavy-tailed distribution of the size of transferred