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As Figure 2.8 shows, in JDK 1.0 there are only three possible paths that byte code can take through the Java security model. The path taken is determined by the origin of the byte code. Built-in byte code from the JDK 1.0 distribution (off the local disk) is allowed to bypass the verification stage. This means that built-in classes are assumed to be both correct and well behaved. All other JDK 1.0 byte code classes must pass through the strict security checks.

As heavy-tailed distributions are increasingly used to represent workload characteristics of computing systems, researchers interested in simulating such systems are beginning to use heavy-tailed inputs to simulations For example, Paxson [12] describes methods for generating self-similar time series for use in simulating network traf c and Park et al [11] use heavy-tailed le sizes as inputs to a network simulation However, an important question arises: How stable are such simulations This can be broken down into two questions: 1 How long until such simulations reach steady state 2 How variable is system performance at steady state In this section we will show that if simulation outputs are dependent on all the moments of the distribution F, then the answers to the above questions can be surprising.

Summary

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Essentially, we show that such simulations can take a very long time to reach steady state; and that such simulations can be much more variable at steady state than is typical for traditional systems Note that some simulation statistics may not be affected directly by all the moments of the distribution F, and our conclusions do not necesssarily apply to those cases For example, the mean number of customers in an M =G=I queueing system may not show unusual behavior even if the service time distribution F is heavy tailed because that statistic only depends on the mean of F Since not all simulation statistics will be affected by heavy-tailed workloads, we choose a simple statistic to show the generality of our observations: the sample mean of the heavy-tailed inputs.

Figure 2.8 There are three possible paths that Java byte code may follow through the JDK 1.0.2/JDK 1.1 security models. The path chosen depends on where the byte code originates and (in the case of JDK 1.1) what signatures the code carries. External byte code (loaded across the network) that is not signed by a trusted party (1) must be Verified and is subject to the Class Loader and the Security Manager. Locally developed byte code (2) is subject to the same checks (unless it is placed on the CLASSPATH). Byte code from the JDK distribution (including other packages placed on the CLASSPATH) and any class that is signed by a trusted party (3) does not pass through the Verifier and may be subject to further optional security checks.

Since our results apply to the sample mean of the input, we expect that any system property that behaves like the sample mean should show similar behavior For example, assume we want to achieve steady state in a particular simulation This implies that the measured system utilization l (where l 1 is the x.

average interarrival time and x is the sample mean of service times over some period) should be close to the desired system utilization r. For this to be the case, x must be close to its desired mean m. To analyze the behavior of the sample mean, we are concerned with the convergence properties of sums of random variables. The normal starting point for such discussions would be the central limit theorem (CLT). Unfortunately, the CLT applies only to sums of random variables with nite variance, and so does not apply in this case. In the place of the CLT we instead have limit theorems for heavytailed random variables rst formulated by Levy [4, 5]. d To introduce these results we need to de ne the notation A 3 B, which means that the random variable A converges in distribution to B (roughly, has distribution B for large n). Then the usual CLT can be stated as follows. For Xi i.i.d. and drawn from some distribution F with mean m and variance s2 < I, de ne An and Zn n1=2 An m ; then Zn 3 n 0; s2 ;

In addition to HIPAA, Gramm-Leach-Bliley, state privacy protection laws, and regulatory requirements imposed in doing business internationally, SOX compliance is just one more reason to use a comprehensive architectural design team and process to create new or improve your current IT systems. IT systems are already complicated, and adding spotty upgrades and patches simply intensifies the complexity without necessarily increasing the security

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