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Finally, if you want to copy a descriptor safely in a way that enables forward and backward compatibility (for example, when communicating capability packages that may evolve between versions), use the following methods:

which (1) shows that the estimate is asymptotically unbiased irrespective of the Gaussian hypothesis and (2) allows us to subtract a rst-order approximation of that bias. Similarly, Var log2 mj 9 2 log2 e 2 1 C4 j =2 ; nj 2:29

sequentialThreshold) = seqFib(n); = new Fib(n - 1); = new Fib(n - 2);

Kern::InfoCopy(TDes8& aDest, const TDesC8& aSrc); Kern::InfoCopy(TDes8& aDest, const TUint8* aPtr, TInt aLength);

which again shows the similarity of form with the corresponding Gaussian case, Eq. (2.26), in the asymptotic limit. It follows that ^ Var a

Fig. 15.15 Results of a connection-by-connection simulation of a connection-admission strategy for a single unit capacity edge contrasted for different interarrival time models. (a) 5214 arrivals taken from a one hour time period from an AT&T dataset. (b) 34,152 arrivals from a CMU dataset.

These provide compatibility by copying only as much data as required by the target descriptor. If the source is longer than the maximum length of the target, then the amount of data copied is limited to the maximum length of the target descriptor. Conversely, if the source is shorter than the maximum length of the target, then the target descriptor is padded with zeros.

coInvoke(f1, f2); }

This means that the Weibull distribution is modeling long breaks in the arrival process as least as well as a nonstationary Poisson process taken over a reasonable period. In addition, the Weibull process is capable of modeling the short interarrival times much more accurately. Figure 15.16 reveals some interesting comparisons of a nonstationary Poisson process to the stationary Weibull process. Figure 15.16(a) shows the actual arrival times of the connections for the arrivals derived from the June dataset. Figure 15.16(b) shows the empirical cumulative distributions of the original interarrival time distribution, the tted Weibull distribution, the tted exponential distribution, and the empirical distribution of the nonstationary Poisson process with period 6 minutes for the June dataset. While Fig. 15.16 shows the result for a speci c nonstationary Poisson process, the results are almost identical for other nonstationary Poisson processes.

If a request is made from user-side code to perform a long running task, control is usually passed back to the user process, while the task completes in a separate kernel thread. Under these circumstances, you are no longer in the context of the user thread when you want to transfer data, so you should use the following methods:

1 0 4

number = f1number + f2number;

10 2

Kern::ThreadDesRead(DThread* aThread, const TAny* aSrc, TDes8& aDest, TInt aOffset, TInt aMode); Kern::ThreadRawRead(DThread* aThread, const TAny* aSrc, TAny* aDest, TInt aSize); Kern::ThreadDesWrite(DThread* aThread, TAny* aDest, const TDesC8& aSrc, TInt aOffset, TInt aMode, DThread* aOrigThread); Kern::ThreadRawWrite(DThread* aThread, TAny* aDest, const TAny* aSrc, TInt aSize, DThread* aOrigThread=NULL); Kern::ThreadDesRead(DThread* aThread, const TAny* aSrc, TDes8& aDest, TInt aOffset); Kern::ThreadDesWrite(DThread* aThread, TAny* aDest, const TDesC8& aSrc, TInt aOffset, DThread* aOrigThread=NULL); Kern::ThreadGetDesLength(DThread* aThread, const TAny* aDes); Kern::ThreadGetDesMaxLength(DThread* aThread, const TAny* aDes); Kern::ThreadGetDesInfo(DThread* aThread, const TAny* aDes, TInt& aLength, TInt& aMaxLength, TUint8*& aPtr, TBool aWriteable);

Fig. 15.16 (a) Arrival sequence of 34,152 arrivals from a CMU dataset. (b) Empirical and tted cumulative distributions of the interarrival times of the simulations.

These all take a handle to the client thread as their rst argument. You must obtain this while you are still in user context (such as when the