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try { for (;;) { taskstake()run(); } } catch (InterruptedException ie) {} // die } })start();

The obvious spikes around 0146 s for the CMU dataset and around 0000234 s for the external AT&T dataset are due to protocol dependencies The spike in the CMU dataset is caused almost entirely by the KSHELL protocol Each KSHELL invocation establishes two TCP connections within a time period of roughly 0146 s Since, on this Ethernet segment, it is quite likely that no other TCP connection request is initiated within such a small time window, this creates a spike in the empirical cumulative distribution The spike in the external AT&T dataset is caused almost entirely by World Wide Web accesses that correspond to an HTTP client establishing parallel TCP connections to load inlined HTTP objects such as gif les Since this external AT&T dataset is dominated by HTTP traf c, this spike is much more prevalent here than in the CMU dataset.

promptly to the events, then it will get many events for that character, and the timer will have generated all but the rst of them. When a new key down event occurs, WSERV must inform the timer, so that it can cancel the current repeat this is needed because any key press should change the character the timer generates. Similarly, when a key up event occurs, WSERV informs the timer, so that it can stop the repeat if the key up comes from the currently repeating character. WSERV calls the keyboard translator object next, using the function:

For the purpose of the analysis discussed in this chapter we did not exclude any of these observations Next, consider the shape and characteristics of the empirical cumulative distribution of all interarrival times Figure 158(a) shows the empirical cumulative distribution resulting from the empirical density function of Fig 157(a) for a CMU trace together with the tted cumulative distributions of the Weibull, Pareto, lognormal (lnorm), and exponential distributions Figure 158(b) shows the cumulative distributions for the external AT&T trace It is immediately obvious that the exponential, Pareto, and lognormal distributions result in very poor ts, while the agreement between the tted Weibull distribution and the measured interarrival times is quite good It is interesting that the exponential model underestimates short as well as long interarrival times, while the lognormal model overestimates both.

protected final Semaphore available = new Semaphore(0); void put(DiskTask t) { insert(t); availablerelease(); } DiskTask take() throws InterruptedException { availableacquire(); return extract(); } synchronized void insert(DiskTask t) { DiskTask q; if (tcylinder >= currentCylinder) { // determine queue q = thisSweep; if (q == null) { thisSweep = t; return; } } else { q = nextSweep; if (q == null) { nextSweep = t; return; } } DiskTask trail = q; // ordered linked list insert q = trailnext; for (;;) { if (q == null || tcylinder < qcylinder) { trailnext = t; tnext = q; return; } else { trail = q; q = qnext; } } }

TBool TranslateKey(TUint aScanCode, TBool aKeyUp, const CCaptureKeys &aCaptureKeys, TKeyData &aKeyData)

The model based on the Pareto distribution overestimates the medium length interarrival times and yields a very poor t for the shorter interarrival times The observation that the tted Weibull distribution yields a much better t than any of the other ones be con rmed the discrepancy measure l2 Table 155 q can q using 2 2 2 ^ ^ ^ ^ ^ ^ summarizes l v l and l v l2 for all ts and three data sets This corresponds to a 6826% con dence interval Figure 159 visualizes the data from Table 155 for all datasets in a bar plot where the height of the bars corresponds to ^ ^ the value of l2 and the error bars to the standard deviation of l2 or the 6826% con dence intervals.

To visualize the discrepancy better, all values above a threshold of ve are cut off The Weibull distribution yields statistically signi cant better models for the interarrival times of all ten datasets For all datasets except the CMU dataset from November 1994 the lognormal model is an order of magnitude better than both the exponential model and the Pareto model, while the three cannot be distinguished for the CMU November dataset This difference between the datasets is most likely due to the fact that they span signi cantly different time periods and capture different traf c mixes 1554 Application-Speci c Interarrival Times.