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Concerning the impact long-range dependence in traf c, what is essential then is the heavy-tailed distribution of the random variables that represent the time scales in the underlying LRD traf c, which is the most important property of LRD traf c In addition, our analysis and numerical results based on the uid traf c model with two states have demonstrated that the loss probability as well as the expected loss ratio in the steady state can be less than those in the transient state This is because steady-state analysis cannot capture actual loss behavior of traf c processes in the transient state In an idealized steady state, the system has forgotten its history As a result, the steady-state loss measures are merely some constants In contrast, transient loss measures depend on the history of the system and therefore are variables.



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On the other hand, there are plenty of incompetent support personnel answering the phones for good vendors. If you are unsatisfied with the response you get back from support, make sure your local sales team knows all about it. (If you like the support you get, tell them that too!) As a customer, you have the right to say, I don t like that support person and I don t want to work with him anymore. Get me someone else. Similarly, if your sales rep or another member of the sales team is a jerk, then you certainly have the right to go to his or her manager and ask for a different rep for the account.





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Within the framework of steady-state performance analysis, loss performance is weighted by the marginal state distribution of the traf c process However, the marginal distribution is not important to transient loss performance Our numerical results have shown that different two-state uid-type traf c processes with the same marginal state distribution but different distributions of S and T can experience signi cantly different transient loss What is important here again is the distributions of the random variables regarding the time scales in the underlying traf c Since steady-state loss performance can be much better than that in the transient state even for Markov traf c, and transient loss performance of Markov.

Whether it s hardware service to repair damaged components or bug fixes needed to prevent repeated system outages, vendor service plays a large role in system recovery and constraining the time to repair a failed system. Here are some issues and questions to consider when evaluating a vendor s ability to support you and your systems.

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traf c can also be much better than that of LRD traf c, prediction of loss performance based on Markov models in the steady state can then lead to even worse underestimation of loss performance degradation for LRD traf c in the transient state.

Telephone support. Nearly all support calls begin with a phone call. When you call the vendor, how long does it take before you speak to anyone How many push-button menu options must you parse before you get to a human being How long before you speak to someone who can help solve the problem How helpful are the people you speak with Do they call you back in a timely manner On-site service. When on-site service is required, how quickly can the vendor get someone to your location Do they meet or exceed contractual obligations What happens if the vendor fails to meet their obligations Are the people they send able to solve the problem themselves, or do they spend all their time on the phone back to headquarters Spare parts. How long does it take for the vendor to get spare parts to you Are the parts stored locally, or must they come in from across the country Root-cause analysis. This is often the trickiest part of problem solving. How good is the vendor at determining the ultimate cause of an outage Do they have tools to help diagnose and solve problems Do they keep going back to you with more questions and requests for ever-increasing amounts of assistance, or can they solve the problem on their own Bug fixes. Once a software problem is isolated, can the vendor provide a patch (and the mechanism to apply the patch), or is an upgrade required

In this chapter, we have presented an analysis of the transient loss performance impact of long-range dependence in network traf c We have examined transient loss behavior for multistate uid traf c in single states and for two-state uid traf c, under two fundamentally different assumptions, that is, Markov versus LRD, on the underlying traf c process Numerical results are given to illustrate the analysis To sum up, the main ndings of this study are as follows In the transient state, different traf c processes exhibit different loss behaviors The essential cause of the difference is the distributions of the times spent by the traf c processes in their states Such distributions characterize the stochastic properties of the basic time scales in the traf c processes Heavy-tailed distributions appear to be a core factor of LRD traf c.

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