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In several examples of processes that we envisage modeling by these methods, there will only be the level process First, the level process may be some smoothed functional of a raw bandwidth process This is the case with algorithms for smoothing stored video by converting into piecewise constant rate segments in some optimal manner subject to buffering and delay constraints; see Salehi et al [25] With such smoothing, the input rate will directly be a level process as we have de ned it Alternatively, the level process may stem from rate reservation over the period between level-shifts, rather than the bandwidth actually used This would be the case for RCBR previously mentioned In this situation we act as if the reservation level is the actual demand, and thus again have a level process.

You would typically use a synchronous request to set or retrieve some state information. Such a request may access the hardware itself, but usually completes relatively quickly. Synchronous requests are initiated by a call to RBusLogicalChannel::DoControl(), which does not return until the request has fully completed.

A key to being able to analyze the system with such complex sources represented by our traf c model is exploiting asymptotics associated with multiplexing a large number of sources The ever-increasing network bandwidth implies that more and more sources will be able to be multiplexed This gain is generally possible, even in the presence of heavy-tailed distributions and more general long-range dependence; for example, see Duf eld [12, 13] for demonstration of the multiplexing gains available for long-range dependent traf c in shared buffers As the scale increases, describing the detailed behavior of all sources become prohibitively dif cult, but fortunately it becomes easier to describe the aggregate, because the large numbers produce statistical regularity As the size increases, the aggregate demand can be well described by laws of large numbers, central limit theorems, and large deviation principles.

int r = resets;

We have in mind two problems: rst, we want to do capacity planning and, second, we want to do real-time connection-admission control and congestion control In both cases, we want to determine whether any candidate capacity is adequate to meet the aggregate demand associated with a set of sources In both cases, we represent the aggregate demand simply as the sum of the bandwidth requirements of all sources In forming this sum, we regard the bandwidth processes of the different sources as probabilistically independent The performance analysis for capacity planning is coarser, involving a longer time scale, so that it may be appropriate to do a steady-state analysis However, when we consider connection-admission control and congestion control, we suggest focusing on a shorter time scale We are still concerned with the relatively long time scale of connections, or scene times in video, instead of the shorter time scales.

An asynchronous request is one which you would typically use to perform a potentially long running operation for example, one that transmits or receives a block of data from the hardware when it becomes available. The time taken for such a request to complete depends on the operation performed, and during this time the client user-side thread may be able to continue with some other processing. Asynchronous requests are initiated by a call to RBusLogicalChannel::DoRequest(), which takes a TRequestStatus object as an argument and normally returns control to the user as soon as the request has been issued. Typically, hardware will indicate completion of an operation by generating an interrupt, which is handled by an interrupt service routine (ISR) provided by the driver. This in turn schedules a DFC, which runs at some later time in the context of a kernel-side thread and signals the client user-side thread, marking the asynchronous request as complete. More than one asynchronous request can be outstanding at the same time, each one associated with its own TRequestStatus object, and each identi ed by a speci c request number. The device driver framework puts no explicit limit on the number of concurrent outstanding asynchronous requests; any limit must be enforced by the driver itself. However, the API to cancel a request uses a TUint32 bit mask to specify the operations to be cancelled, which implicitly prevents you from uniquely identifying more than 32 concurrent request types.

of cells or bursts, but admission control and congestion control are suf ciently shortterm that we propose focusing on the transient behavior of the aggregate demand process In fact, even for capacity planning the transient analysis plays an important role The transient analysis determines how long it takes to recover from rare congestion events One application we have in mind is that of networks carrying rate-adaptive traf c In this case the bandwidth process could represent the ideal demand of a source, even though it is able to function when allocated somewhat less bandwidth So from the point of view of quality, excursion of aggregate bandwidth demand above available supply may be acceptable in the short-term, but one would want to dimension the link so that such excursions are suf ciently short-lived.

do { wait(); } while (resets == r); } else { // trip count = parties; // reset count for next time ++resets; notifyAll(); // cause all other parties to resume } return index; } }