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(The ability to override stop, or not, is now enforced by policy) It is ironic that some of the most Java-heavy Web pages almost go as far as denial of service in doing what their programmers intended Some Java-enhanced sites take quite a while to load (we have heard of some applets that take 10 minutes to start up, even over a very fast T1 connection) The bottleneck likely involves the byte code Verification process and not the network transmission time Of course, slow loading/verifying really doesn't constitute a true denial-of-service attack Even a denial-of-service attack that is no big deal when embedded in an applet becomes a serious problem if it can be launched against a Web server Sites that use Java-enabled Web servers and configure those servers to allow anyone to upload a servlet are exposing themselves to easy denial-of-service attacks.

However, as the overall aggressiveness level is further increased, the cost of aggressiveness begins to outweigh its bene t and we observe a downward shift in the unimodal throughput curve SAC is designed to operate under all three network conditions, nding a nearoptimum throughput in each case The most challenging task arises when the.

Fig. 18.12 Shape of throughput curve as a function of aggressiveness threshold for three levels of background traf c: (a) 2.5 Mb=s, (b) 5 Mb=s, and (c) 7.5 Mb=s.

Identity management (dealing with identity in the modern enterprise) Meta-directories (leveraging to reduce administration and improve access controls) Identity provisioning (value provided, accuracy gained) Authentication (options and limits) Access controls (fine-grained controls necessary to protect data and privacy)

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In short, anyone, anywhere in the world, can bring down such a server whenever he or she feels like it That's why we recommend that Java-enabled servers only accept servlets from clients that they really trust..

Fig. 18.13 Change in shape of throughput curve as the aggressiveness schedule E is shifted (upward) by 0.5, 2.0, 4.0, 20.0.

network con guration leads to a unimodal throughput curve for which nding the maximum throughput is least trivial That is, neither blindly applying aggressiveness nor absteining from it are optimal strategies SAC's adaptivity is also useful in nonstationary situations where the network con guration can shift from one quasistatic throughput state to another 18532 Perfect Prediction, Uncertainty, and Aggressiveness Now that we have shown that selective aggressiveness can help but indiscriminate aggressiveness can hurt, we seek to understand three further aspects of SAC: (1) how much performance is gained by applying selective aggressiveness (vis-a-vis not applying at all), (2) how much performance is lost due to prediction inaccuracies, and (3) what is a practical aggressiveness schedule to use since we cannot assume to know the aggressiveness threshold for which maximum throughput is achieved (when using a threshold schedule).

... Preface -- 1 -- 2 -- 3 -- 4 -- 5 -- 6 -- 7 -- 8 -- 9 -- A -- B -- C -- Refs Front -- Contents -- Help

The practical aggressiveness schedule that we found effective is the inverse schedule given by E x 1=x That is, the magnitude of aggressiveness is inverseproportionally diminished as a function of the expected future traf c level To measure the performance loss due to inaccuracies arising from the using perconnection on-line prediction of future traf c levels, we observe the performance of SAC when, instead of using the on-line CondProb table, a perfect knowledge of future aggregate traf c is assumed and employed in conjunction with the inverse schedule Finally, to compare the net gain of having used a practical version of SAC in this case, predicted future using per-connection on-line table and inverse aggressiveness schedule we observe the generic linear increase=exponential decrease feedback congestion control without SAC active Figure 18.

Readers will learn what it takes to design an information technology infrastructure capable of protecting the privacy and access integrity of computer data, particularly in the Web applications environment.

14(a) shows the original throughput versus threshold schedule curve superimposed with the throughput achieved by using SAC with perfect future knowledge and inverse aggressiveness schedule (topmost line), using SAC with predicted future and inverse schedule (middle line), and using the generic linear increase=exponential decrease feedback congestion control without SAC (bottom line) We observe that the generic feedback congestion control performs worst among the four we are counting the family of SAC algorithms for the threshold schedule as one which is mainly due to the costly nature of exponential backoff when coupled with conservative linear increase For our purposes, the absolute magnitudes do not matter so much as the relative magnitudes, which demonstrate a qualitative performance relationship SAC with perfect information and inverse epsilon schedule achieves the highest throughput (even higher than the peak threshold schedule throughput) and SAC with predicted future and inverse schedule achieves a performance level in between.

Copyright 1999 Gary McGraw and Edward Felten. All rights reserved. Published by John Wiley & Sons, Inc.

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