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More importantly, we observe that the throughput gain relative to the generic feedback congestion control is about 20% in the a 1:05 case versus about 4% for the a 1:95 case This indicates that selfsimilar burstiness although detrimental to network performance, in particular, QoS possesses structure that can be exploited to dampen its negative impact In fact, the more long-range dependent, the more structure there is to exploit effectively An important point to note is that we have held the mean of the background traf c levels for both a 1:05 and a 1:95 constant to achieve comparability This is a nontrivial matter since for a 1:05, the mean traf c level estimated by using the Pareto distribution will overestimate the sample mean observed in practice, even if the system is run for 10,000 seconds Figure 18.

17 shows the predictability gain in terms of throughput achieved for four background traf c cases a 1:05, 135, 165, 195 Interestingly, the throughput gain shows a superlinear increase as a approaches 1 (ie, becomes more long-range dependent)..

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Fig. 18.16 (a) Under a 1:05 traf c, the performance improvement is about 20% when using SAC with on-line table and inverse schedule. (b) Under a 1:95 traf c, the performance improvement is only 4%.

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6 takes a look at how the features present in directory technology, directory schema, and meta-synchronization can be leveraged in a complementary way to improve systems security profiles. 7 covers the logic, core critical elements, and the relationships that must be used to successfully achieve identity management and identity provisioning in the Web applications environment. It also examines how these elements are leveraged within IT and business processes alike to achieve granular control over data and information. 8 puts together the information from the previous chapters and discusses ways to build support and integrate the architecture into legacy, current, and future applications in business and government organizations. 9 discusses the protection target data information and how it will and must be related and linked to the identities of the people and systems that will be allowed access. 10 brings it all together in the Web applications environment. 11 discusses the shortcomings of federated identity schemes and the risks of relying on outside sources for permitting access. The final chapter, 12, explores a future where every access control system across the Web is tied to AAA (authentication, authorization, and accounting) servers for access to sensitive systems and data a future in which privacy boundaries are respected, systems are capable of enforcing them, and digital credentials can be trusted.

Fig. 18.17 traf c. Performance gain due to predictability for a 1:05, 1.35, 1.65, 1.95 background

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18534 Convergence Rate and Performance In Section 18523 we have shown the rapid convergence rate of the on-line conditional probability table The faster the convergence, the earlier the conditional probabilities can be employed for congestion control purposes, resulting in higher throughput gain Other things being equal, early activation of SAC induces a trade-off relation between the bene t obtained by applying predictive information for congestion control and the cost of engaging SAC based on possibly inaccurate conditional probability estimates Figure 1818(a) shows the impact of inaccuracies in the conditional probability density estimates on performance The top graph plots throughput as a function of training time that is, time spent in estimating the conditional probability densities when the conditional probability table is subsequently xed and used in a 10,000 second throughput measurement run.

This allows us to assess the impact of inaccurate prediction estimates on throughput performance As the top graph shows, convergence is rapid after which the incremental gain observed via further accuracies saturates Due to rapid convergence, the net gain in throughput due to increased prediction accuracy is below 5% Contrast this with the bottom two graphs of Fig 1818(a), which show 10,000 second throughput measurements when the conditional probability table used is that of a 1:95 traf c (trained over 10,000 seconds) and randomly, respectively The gap shows that even inaccurate prediction estimates are signi cantly more useful than random or otherwise-structured information for performance enhancement purposes The fast convergence property can be further explained by Fig 1818(b), which plots the computed conditional expectation E L2 j as a function of training time.

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