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Broadcasts are huge consumers of bandwidth is a rather common, and incorrect, statement throughout our industry. A broadcast or multicast packet takes up exactly as much bandwidth as a unicast packet of the same number of bytes. It is true that broadcast storms consume significant bandwidth. Even in broadcast storms, the negative effect is as likely to be due to the host processing load in handling many broadcasts as it actually is to bandwidth starvation (assuming the hosts are connected to a fast LAN). It is also true that some obsolete protocols, such as NetBEUI and NetWare 3.x, send frequent broadcasts as part of their resource location mechanism. Such broadcasts are acceptable with typical medium speeds of small LANs, the environment for which these mechanisms were developed. The bandwidth load is not inherently high simply because it consists of broadcast or multicast packets. Rather, the large load is due to the higher-level resource location protocol periodically sending information that is not needed. Appropriately used, broadcasts and multicasts actually save bandwidth, when they are used as an alternative to sending multiple unicasts to a group of sources.

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After two iterations, we get the final results: Tr(3) = 2.57, TZ(3) = 1, X(3) = 0.84, x1(3) = 2.16, &(3) = 0.84.

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R2 is the same value as X. Thus, the outcome of a sequence of two XORs using the same value produces the original value. To see this feature of the XOR in ...

When it comes to intellect, you re not searching for the next Einstein but trying to get at this: Is this person bright, articulate, and intuitive It s easy to get a general sense of how smart someone is an interview; what counts is the individual s ability to apply that intellect to diverse situations. You can readily nd highly intelligent people with no selfawareness and lots of ego, and they will almost always hurt you in selling situations. To assess an individual s likely behavior in those situations, which is what you really must learn, pose speci c problems for solution. Use examples that let you see how a person thinks under pressure and organizes a response. Maybe you disguise a dif cult sales situation you faced in the past and ask how the person would address it. As you consider possible questions to test intellect, ask those that call for the person to use creativity. When presenting the problem, eliminate obvious remedies from consideration. Explain several solutions that the client already tried, and then ask the candidate to come up with a different remedy. Once you get a response, pose follow-ups: How would you develop this solution What constraints are important to consider What else would you need to know to be sure of your answer Some people will ace these scenarios; others will experience meltdown. You can guess which ones you want on your team.

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Research Queueing package RESQ, which offers a so-called split node that splits a job into two tasks that proceed independently through the network. This node type was used to model the overlap of jobs. In Table 10.25, 10.27, and 10.26, the results of the comparison of discrete-event simulation (DES) with the approximate analytic method are shown. Both absolute errors as well as relative errors are given. For each set of models, the mean and maximum absolute and relative errors are listed for utilizations, queue lengths, throughputs, and response times (for the terminal models). As we can see from the results, the approximation is particularly accurate for estimating utilizations and throughputs. For these quantities, the relative error is about 1.3010, and the maximum relative error is 17.4%. We can also see that the estimates for the mean queue length are somewhat less accurate, the mean relative error is 4.2%. The maximum relative error is very high (up to 89%). Th ese high errors always occur in very imbalanced systems such as the central-server model sets V and VI (in these sets the overlap factor is very high). If, on the other hand, the model is better balanced (lower values of fOl), we get quite accurate approximations. In this case the mean relative error for all performance measures is 1.9%, and the maximum relative error is 2.9%.

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