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We would be irresponsible not to note that with code signing comes a host of new risks to manage. Most notable among the risks are two: first, that the implementation will have holes (JDK 1.1 code signing has already fallen prey to this risk); and second, that security policies will get too complicated to understand and manage.

and hence is clearly heavy tailed. It is not dif cult to check that for a 2 heavy-tailed distributions have in nite variance, and for a 1, they also have in nite mean. Thus, as a decreases, a large portion of the probability mass is located in the tail of the distribution. In practical terms, a random variable that follows a heavy-tailed distribution can take on extremely large values with nonnegligible probability. 18.2.2 Structural Causality

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In Park et al. [33], we show that aggregate traf c self-similarity is an intrinsic property of networked client=server systems where the size of the objects (e.g., les) being accessed is heavy-tailed. In particular, there exists a linear relationship between the heavy-tailedness measure of le size distributions as captured by a the shape parameter of the Pareto distribution and the Hurst parameter of the resultant multiplexed traf c streams. That is, the aggregate network traf c that is induced by hosts exchanging les with heavy-tailed sizes over a generic network environment running ``regular'' protocol stacks (e.g., TCP, ow-controlled UDP) is self-similar, being more bursty in the scale-invariant sense the more heavy-tailed the le size distribution are. This relationship is shown in Fig. 18.1. The relationship is robust with respect to changes in network resources (bandwidth, buffer capacity), topology, the in uence of cross-traf c, and the distribution of interarrival times. We call this relationship between the traf c pattern observed at the network layer and the structural property of a distributed, networked system in terms of its high-level object sizes structural causality [33]. H 3 a =2 is the theoretical value predicted by the on=off model [42] a 0=1 renewal process with heavy-tailed on or off periods assuming independent traf c sources with no interactions due to sharing of network resources. Structural causality is of import to self-similar traf c control since (1) it provides an environment where self-similar traf c conditions are easily facilitated just simulate a client=server network (2) the degree of self-similar burstiness can be intimately controlled by the application layer parameter a, and (3) the self-similar network traf c induced already incorporates the actions and modulating in uence of

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

the protocol stack since the observed traf c pattern is a direct consequence of hosts exchanging les whose transport was mediated through protocols (e.g., TCP, owcontrolled UDP) in the protocol stack. This provides us with a natural environment where the impact of control actions by a congestion control protocol can be discerned and evaluated under self-similar traf c conditions.

18.3 18.3.1

Appendix E Documentation Examples Guiding Principles Policies Standards Procedures Best Practices Guidelines Go with the Flow Appendix F Sample Job Description for Directory Engineer/ Schema Architect Index

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

In this section, we show that the correlation structure present in long-range dependent (LRD) traf c can be detected and used to predict the future over time scales relevant to congestion control. Time series analysis and prediction theory have long histories with techniques spanning a number of domains from estimation theory to regression theory to neural network based techniques to mention a few [3, 17, 22, 40, 44, 45, 49]. In many senses, it is an ``art form'' with different methods giving variable performance depending on the context and modeling assumptions. Our goal is not to perform optimal time series prediction but rather to choose a simple, easyto-implement scheme, and use it as a reference for studying congestion control techniques and their ef cacy at exploiting the correlation structure present in LRD traf c for improving network performance. Our prediction method, which is described next, is a time domain technique and can be viewed as an instance of conditional expectation estimation.

Assume we are given a wide-sense stationary stochastic process xt tPZ and two numbers T1 ; T2 > 0. At time t, we have at our disposal a P

qi ;

CHAPTER SECTIONS: 1 / 2 / 3 / 4 / 5 / 6 / 7 / 8 / 9

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where qi is a sample path of xt over time interval t T1 ; t . For notational clarity, let V1 P

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