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Basic ground-level action code is defined in non-public methods (By convention, we name the non-public version of any method method as doMethod) Somewhat less flexibly, these methods need not be declared non-public if they are instead designed to be overridden in subclasses Before and after operations are also defined as non-public methods Public methods invoke the ground methods between the before and after methods

In the former case, although the overall mean rate is accurately predicted, it is hardly a useful traf c characteristic since the rate averaged over periods of several seconds can be signi cantly different In the latter, the rate information is insuf cient to allow signi cant statistical multiplexing gains For elastic ows it is, by de nition, possible to shape traf c to conform to the parameters of a leaky bucket However, it remains dif cult to choose appropriate leaky bucket parameters If the traf c is long-range dependent, as in the case of an aggregation of ows, the performance models studied in this book indicate that queueing behavior is particularly severe.

The client API uses a global instance of the DMyController object, which is initialized in the extension entry point:

For any choice of leak rate r less than the peak rate and a bucket capacity s that is not impractically large, the majority of traf c will be smoothed and admitted to the network at rate r The added value of a nonzero bucket capacity is thus extremely limited for such traf c We conclude that, for both stream and elastic traf c, the leaky bucket constitutes an extremely inadequate descriptor of traf c variability..

Applying this to the Tank example leads to:

DECLARE_STANDARD_EXTENSION() { DMyController* TheController = new DMyController(); if(TheController == NULL) return KErrNoMemory; return TheController->Create(); }

To perform admission control based solely on the parameters of a leaky bucket implies unrealistic worst-case traf c assumptions and leads to considerable resource allocation inef ciency For statistical multiplexing, ows are typically assumed to independently emit periodic maximally sized peak rate bursts separated by minimal silence intervals compatible with the leaky bucket parameters [8] Deterministic delay bounds are attained only if ows emit the maximally sized peak rate bursts simultaneously As discussed above, these worst-case assumptions bear little relation to real traf c characteristics and can lead to extremely inef cient use of network resources An alternative is to rely on historical data to predict the statistical characteristics of know own types This is possible for applications like the telephone, where an estimate of the average activity ratio is suf cient to predict performance when a set of conversations share a link using bufferless multiplexing.

It is less obvious in the case of multiservice traf c, where there is generally no means to identify the nature of the application underlying a given ow The most promising admission control approach is to use measurements to estimate currently available capacity and to admit a new ow only if quality of service would remain satisfactory assuming that ow were to generate worst-case traf c compatible with its traf c descriptor This is certainly feasible in the case of bufferless multiplexing The only required ow traf c descriptor would be the peak rate with measurements performed in real-time to estimate the rate required by existing ows [11, 14] Without entering into details, a suf ciently high level of utilization is compatible with negligible overload probability, on condition that the peak rate of individual ows is a small fraction of the link rate.

abstract class AbstractTank implements Tank { protected void checkVolumeInvariant() throws AssertionError { // identical to AdaptedTank version } protected abstract void doTransferWater(float amount) throws OverflowException, UnderflowException; public synchronized void transferWater(float amount) throws OverflowException, UnderflowException { // identical to AdaptedTank version except for inner call: // try { doTransferWater(amount); } // }

So all we need to do now is provide the functionality. The simple example that follows simply registers the client callback and talks to the hardware to perform the operation (the detailed mechanism isn t shown here). Upon receiving an interrupt, a DFC is queued within which the client s callback function is invoked:

The latter condition ensures that variations in the combined input rate are of relatively low amplitude, limiting the risk of estimation errors and requiring only a small safety margin to account for the most likely unfavorable coincidences in ow activities For buffered multiplexing, given the dependence of delay and loss performance on complex ow traf c characteristics, design of ef cient admission control remains an open problem It is probably preferable to avoid this type of multiplexing and to instead use reactive control for elastic traf c..