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maximize throughput. For any reasonable assignment of bandwidth, buffer size, mean le request size, and other system parameters, we found that by either adjusting the number of clients or the mean of the idle time distribution between successive le transfers appropriately, any target contention level could be achieved. In a typical con guration, the rst 32 connections served as ``background traf c,'' transferring les from clients to servers (or sinks), where the le sizes were drawn from Pareto distributions with shape parameter a 1:05, 1.35, 1.65, 1.95. As in Park et al. [33], there was a linear relation between a and the long-range dependence of aggregate traf c observed at the bottleneck link G1 ; G2 as captured by the Hurst parameter H. H was close to 1 when a was near 1, and H was close to 1 when a 2 was near 2. The 33rd connection acted as an in nite source trying to maximize throughput by running the generic feedback control, with or without SAC. In other settings, the number of congestion-controlled in nite sources was increased to observe their mutual interaction and the impact on fairness and ef ciency. A typical run lasted for 10,000 or 20,000 seconds (simulated time) with traces collected at 10 ms granularity.

An organization s guiding principles should be well known enough to direct the systems architecture team in its designs, and ultimately to direct the operation of the firm s IT department. Unfortunately, many organizations may not have taken the time to capture, record, or publish their standards. Examples of guiding principles include general statements such as:

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18521 Predictability Structure One of the rst items to test was estimation of the conditional probability densities PrfL2 jL1 g using the per-connection, on-line method described in Section 18421 We observe the same skewed diagonal shift characteristics as seen in the off-line case for a 1:05 traf c and the relatively invariant shape of the probability densities for a 1:95 traf c (we omit the 3D plots due to space constraints) Also, as in the off-line case, as we increase the time scale (ie, T1 ) from 500 ms to 1 s and higher, for a 1:05 traf c the probability densities becomes more concentrated, thus increasing the accuracy of prediction Figure 189(a) shows the shifting effect of the conditional probabilities for a 1:05 traf c via a 2D projection that shows the marginal densities.

5. When the thread wakes up, have it begin calculating in an infinite loop (or some other CPUintensive activity that eats cycles). Such a computation will, in effect, bring the browser down by taking away all available computational resources. One particularly worthy function for intense calculation overload is the Ackerman function shown in Listing 4.4.

Whereas the shifting effect is evident for a 1:05 traf c, for a 1:95 traf c (Fig 189(b)) the probability densities stay largely invariant 18522 Time Scale In Section 1832, in connection with the off-line estimation, we showed using entropy calculations that the conditional probability densities became more concentrated as time scale was increased, attening out eventually near the 4 5 s mark We observe a similar behavior with respect to the entropy curve in the on-line case Locating the knee of the entropy curve is of import for on-line prediction and its use in congestion control since the size of the time scale will in uence whether a certain procedure for exploiting predictability will be effective or not.

As an extreme case in point, if the time scale of prediction were, say, 1000 s, it is dif cult to imagine a mechanism that would be able to effectively exploit this information for congestion control purposes too many changes may be occurring during such a time period that may be both favorable and detrimental to a constant control action yielding a zero net gain On the other hand, if control actions capable of spanning large time frames such as bandwidth reservation or pricing-based admission control were made part of the.

If the project contributes to profit or savings beyond internal cost of capital, proceed. Board of directors must approve projects requiring payback periods beyond two years. Promote electronic access to information and online services without compromising privacy or security. Include failover and disaster recovery capability in every critical system design.

Listing 4.4 The Ackerman function implemented as a Java application. Ackerman takes integer n and exponentiates n by itself n times. This means that Ackerman(3) is equivalent to three cubed, cubed. The program can be run by typing the command java Ackerman n, where n is an integer. Computing anything greater than Ackerman(3) takes a long time and many CPU cycles. This code was written to be as inefficient as possible. This version of Ackerman could easily be used in a denial-of-service attack.

Fig. 18.9 (a) Shifting effect of conditional probability densities P L2 jL1 1 and P L2 jL1 8 under a 1:05 background traf c. (b) Shifting effect of conditional probability densities P L2 jL1 1 and P L2 jL1 8 under a 1:95 traf c.

model, then even large time scale predictability may be exploited, with some effectiveness, for performance enhancement purposes In the present context, we set T1 2 s when incorporating predictability into feedback congestion control via SAC Our experience with different traf c traces suggests that the knee of the entropy curve, for many practical situations, may be located in the 1 6 s range 18523 Convergence Rate of On-Line Conditional Probabilities When using conditional probability densities computed from per-connection, on-line estimations, it becomes important to know when the estimations have converged or stabilized If inaccurate information is used for selective aggressiveness control, it is possible that rather than helping improve performance, it may hurt performance SAC uses a distance measure to decide whether a particular conditional probability density is stable enough to be used for congestion control or not.

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