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public Runnable setter(final Callable function) { return new Runnable() { public void run() { try { set(functioncall()); } catch(Throwable e) { setException(e); } } }; } synchronized void set(Object result) { value = result; ready = true; notifyAll(); } synchronized void setException(Throwable e) { exception = new InvocationTargetException(e); ready = true; notifyAll(); } // other auxiliary and convenience methods }

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Fig. 15.2 Scaling analysis of arrival process of HTTP connection arrival for subsets of our datasets. Note the different labeling at the bottom and the top of the plots: scale j (bottom, j 1 is nest scale); actual time (top, in seconds). The vertical bars at each scale represent 95% con dence intervals for log2 Ej .

In 1, Introducing EKA2, I introduced the various hardware and software components that make up a typical Symbian OS device, and showed the modular architecture as in Figure 12.1.

Fig. 15.3 Scaling analysis of arrival process of FTP CONTROL and HTTP connection arrival for dataset DEC-PKT-4.

The FutureResult class can be used directly to support generic Futures or as a utility in constructing more specialized versions As an example of direct use:

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parameter estimation of 0.5, which is consistent with a Poisson process. Yet, even in this dataset, the process that counts the number of HTTP connections is consistent with a self-similar process with Hurst parameter of H 0X65. To underline the observations that the number of Web connection arrivals is consistent with asymptotic self-similar behavior, Fig. 15.4 shows the results of the scaling analysis for data collected on AT&T WorldNet's network. The rst dataset was collected on 14 August 1998 at 10:30 p.m. on a T3 backbone link while the second dataset was collected on 23 July 1997 at 7 p.m. on a FDDI ring carrying traf c from roughly 420 modems used by WorldNet dialup customers (for more

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Fig. 15.4 Scaling analysis of arrival process of HTTP connection arrival for commercial ISP (AT&T WorldNet).

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details see Feldmann et al. [13, 15]). In the rst dataset 87.6% of the TCP connections are Web connections, of which there are a total of 1,187,866. In the dial dataset at least 85.8% of all TCP connections are Web connections, of which there are a total of 51,027 Web connections. Again both dataset as well as many others show that the arrival process is consistent with self-similarity with a Hurst ^ parameter of H % 0:7. 15.5 CHARACTERIZATION OF CONNECTION INTERARRIVAL TIMES

private final Renderer renderer = new StandardRenderer(); // public void show(final URL imageSource) { try { FutureResult futurePic = new FutureResult(); Runnable command = futurePicsetter(new Callable() { public Object call() { return rendererrender(imageSource); } }); new Thread(command)start();

In this section we t distributions with heavy tails to the empirical distribution of TCP connection interarrival times and explore their goodness of t. To this end we rst outline the methods used to analyze interarrival time distribution. Next, we show that the Weibull distribution in particular gives a good t for TCP connection interarrival times and nish by discussing recent results of using heavy-tailed distributions in traf c modeling. 15.5.1 Modeling of Empirical Distribution

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Until recently [33 35, 42] traf c has almost exclusively been modeled by exponential distributions In the last few years it has been shown that this is insuf cient and that heavy-tailed distributions are more appropriate and can be used to explain selfsimilar behavior or long-range dependent behavior Given a random variable X with distribution function F x P X x , its distribution function F x is called heavy tailed if its tail 1 F x P X > x decreases subexponentially for large values of x This means that if a random variable represents waiting time, then for a random variable with a heavy-tailed distribution the longer the already accumulated waiting time is the lower in the likelihood of the next arrival within the following time interval The models we consider are based on the exponential, the Weibull, the Pareto, or the lognormal probability distributions.

displayBorders(); displayCaption(); displayImage(((Pic)(futurePicget()))getImage()); } catch (InterruptedException ex) { cleanup(); return; } catch (InvocationTargetException ex) { cleanup(); return; } }

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Past work has concentrated on the exponential, the lognormal, and the Pareto distribution, but not the Weibull distribution Often, if the conditions of ``strict randomness'' of the exponential distribution are not satis ed, the Weibull distribution is a suitable alternative [23] The Weibull distribution is a generalization if the exponential distribution in the sense that a variable x has a Weibull distribution of y x=a c has an exponential distribution with probability density function p y e y As the value of c decreases the probability of longer as well as shorter values increases, and the burstiness of the traf c increases The de nitions and the maximum likelihood estimators for all the probability distributions can be found in Tables 153 and 154 For more details see Johnson and Kotz [23] Note that all but the exponential distributions are twoparameter distributions and can have heavy tails.

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I have tried the barcode control for BIRT , adding an EAN - 13 as a type and giving this barcode : 9002490100070, i get the following error : BarcodeItem (id = 73): ...