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to disseminate related information, and algorithms for engineering an optimal path between end points. Another way of defining the optical control plane is to describe it as the capability that provides the functionality traditionally relegated to the centralized network management plane but which is now provided using more distributed methods (this is consistent with the requirements of Grid computing as well). Many of these functions are low-level capabilities that require direct interaction with physical devices, and which require extremely fast responses in comparison with traditional manual operations. Implementing specific control functions in the distributed control plane rather than in the centralized management plane should have the following benefits: a speedup of reaction time for most functions; increase in control scalability; a reduction in operational time and costs; more agility in the behavior of the optical network.



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More serious examples of queuing systems include hospital emergency rooms or organ transplant lists If one is waiting with a possibly broken foot in the emergency room, chances are that the gunshot victim wheeled in later will get treated rst, a last in, rst out queue Similarly, although we would like organ transplant waiting lists to be fair, a rst come, rst served queue, factors such as blood work and donor compatibility can result in treatment being received in a different order than indicated by the transplant list Many times, queuing systems affect us in secondary ways For example, as a result of the west coast dockworkers strike of 2002, consumers were short of certain goods Some companies planned ahead for the strike using tools from inventory modeling or supply chain management, an application of queuing.





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In the absence of censoring, (411) reduces to the empirical survivor function (410) This survivor function estimator is commonly used and is easily computed with standard statistical software In reliability analysis it is denoted by R(t), the random time T is called the event time, ni is the number of components that have not experienced the event at time ti , and di is the number of components that have experienced the event at time ti Example 42: Survivor Function Estimation We return to the HIV blood transfusion model of Example 210 The transfusion data, from the San Francisco Men s Health Study (SFMHS), consist of people who were given HIV-infected blood products Detailed descriptions of the data are in Huzurbazar and Huzurbazar (1999) and Longini et al (1989).

The anticipative strategy strives to produce an IT solution that is prepared for the future, whatever that future may look like. Whether

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The data were collected from 1978 to 1980 and have been used to model the early stages of progression for people with HIV infection Figure 213 presented the owgraph model for these data State 0 is HIV infection with negative antibody status, state 1 is HIV infection with positive antibody status but without AIDS symptoms, and state 2 is pre-AIDS symptoms The transfusion data are informative about the early stages of HIV infection because the exact time of transfusion is known Huzurbazar and Huzurbazar (1999) use the transfusion data to illustrate saddlepoint methods in a frequentist setting and present a censored data histogram as part of their analysis The data consist of 90 people who received HIV-infected blood or blood products through transfusion Seventy people made the transition from state 0 to state 1 For 35 of these 70 people, there is no information beyond state 1.

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For the remaining 35 people, 22 made the transition from state 1 to state 2, and 13 were right-censored in state 1 In other words, these 13 people were last seen in state 1 and they were not observed to make the transition to the end state, state 2 For this example, we will only use the time spent in state 1, which is HIV infection, antibody positive status The data consist of 35 people Of these, 22 made the transition from state 1 to a progressively worse state 2 which is characterized by pre-AIDS symptoms Thirteen people were observed for a period of time in state 1 and then right-censored while in state 1 Figure 45 shows the Kaplan Meier survivor function estimate for these data The death times and survivor function are also given following the R program commands of Figure 46.

For example, Boeing s Wichita plant made arrangements to ship inventory by air rather than by sea so as to have minimal impact on their production lines (cf Wichita Business Journal, 2002) The production lines themselves are also queuing systems During the strike, ships could be seen queued up in the ocean off the Los Angeles coast Even after the strike was settled, it took weeks for the backlog to clear With such a variety of interesting applications, why are queuing systems.

Patient Table Condition C1 High Very high High Medium Very high High Condition C2 Fair Good Good Fair Fair Good Condition C3 Medium Medium Low Medium Low Low Illness y1 y1 y1 y2 y1 y2

R outputs a survivor function value for every data point, without regard to whether an event occurred at that time point Users of S-plus or SAS should note that these packages.

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