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In this section we consider multiplexing one long-tailed on=off process with exponential processes in a uid queue. In Boxma [12, 13], a precise asymptotics of the embedded queue distribution was obtained for multiplexing on=off sources, one of which had regularly varying on periods, while the others had exponentially distributed on periods. A similar setting with intermediately varying on periods was investigated in Rolski et al. [52]. Jelenkovic and Lazar [35] observed that this queueing system is asymptotically interchangeable with a queueing system in which the on=off process is arriving alone and the exponential processes are replaced by their mean values. This result has been generalized in Agrawal et al. [2]. The title of this subsection is borrowed from the title of their paper. In the remainder of this section, a result from Jelenkovic and Lazar [35] is presented. In order to state the result, the following de nitions are introduced. De nition 10.4.4. F P ir if A distribution function F is intermediate regular varying F dt : F t

Traf c engineering for a multiservice network handling both stream and elastic traf c is still a largely unexplored eld. In this section we suggest how it may be possible to generalize the methods and tools developed over the years for dimensioning the telephone network. 16.6.1 Provisioning for Stream Traf c

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reduced to a default. The whole game has changed. Tracing the history of this change as we do in this chapter can lend some important perspective. Before we dig into the complex issues of code signing and trust models, it does us good to review what it is we're trying to achieve in the first place. After all, the point of all this highfalutin' architecture is not to make the world's most complicated system. The real objective is securing mobile code. After we remind ourselves of the main goal of the new security model, we are ready to trace its evolution. We will begin by explaining the enhancements added to Java with the release of JDK 1.1, and go on to discuss the Java 2 model in detail.

To determine the network capacity required to meet a target blocking probability for stream ows, it is necessary to make assumptions about the arrival process of new demands, their rate, and their duration For illustration purposes, we consider a simple traf c model consisting of one link receiving traf c from a very large population of users Details and more general models may be found in Roberts et al [26] for example First assume that it is possible to identify m distinct homogeneous classes, ows of each class having a common rate distribution Flows from class i arrive according to a Poisson process of intensity li (requests per second) and have an expected duration of 1=mi seconds Their peak rate is pi .

Data confidentiality Data integrity Privacy protection Personally identifying information Personal financial Personal medical information Workflow Web access control Extranet access Internet portal access Directory-enabled access controls Finite access control OS access Network access Host access Application access Folder/subdirectory access File access Database access Data field access

For a xed (fairly large) link capacity c, the impact of a ow of class i on the probability of data loss can be summarized in a single gure, the effective bandwidth: the effective bandwidth ei is such that the P probability of data loss is negligible (less than a target value) as long as ni ei c, where ni is the number of class i ows in progress Although measurement-based admission control does not rely on the identi cation of the different classes (a new ow is denied access if its peak rate is greater than a real-time estimate of available bandwidth), for dimensioning purposes we can P assume a ow of class j will be blocked if ni ei > c ej With this blocking condition and the assumption of Poisson arrivals, the distribution of the ni has a wellknown product form enabling computation of the blocking probability.

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Note that blocking probabilities and data loss rates are insensitive to the distribution of ow duration A reasonable approximation for the blocking probability of a ow with peak rate pi when c is large with respect to the ei is given by Bi % where a P ei li =mi , d P pi E a=d; c=d ; d 16:1.

e2 li =mi =a and i E a; n an . P ai n! i n i!

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

is Erlang's formula. Formula (16.1) is a simpli cation of the formulas given by Lindberger [20]. It is less accurate but more clearly demonstrates the structural relationship between performance and traf c characteristics. Instead of identifying traf c classes with

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