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Figure 7.5. Comparison of the end-to-end delay in a globally aware routing. (This gure is taken from the results obtained in Kolar and Abu-Ghazaleh [54].)

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Systems, products, and services have degrees of perfection as viewed by the User and System Developer. System Developers employ design veri cation and validation practices to discover any latent defects and de ciencies early in the System Development Phase when corrective actions are less costly. Despite the best of human attempts to perfect systems, some latent defects and de ciencies remain hidden until someone discovers the problem during the System O&S Phase hopefully WITHOUT adverse or catastrophic EFFECTS. New systems, especially large complex systems, inevitably have latent defects and de ciencies. Sometimes these are minor; other times they are major. From a program and SE perspective, the critical operational issue (COI) is to ensure that the delivered EQUIPMENT and its operators are able to achieve their mission objectives without subjecting them to injury, damage, or threats that jeopardize the mission. Software-intensive systems are especially prone to latent defects that are not discovered during system veri cation testing. Some defects or de ciencies not discovered until a system is elded. Sometimes they are highly obvious, and sometimes they are only detected over a period of time. The discovery may occur directly or indirectly during analysis of large amounts of data. Therefore, monitor Problem Reports (PRs) closely to determine if there are latent defects that need to be corrected and, if so, the degree of urgency in correcting them.





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In the following, we show sample results from the model developed by Kolar and Abu-Ghazaleh, to illustrate the performance difference between globally coordinated routing, and traditional greedy routing approaches. The results of the routes using the model were simulated in a Qualnet [32] simulator using an IEEE 802.11b MAC protocol. Figure 7.5 shows 6 6 and 8 8 grid topology results. The end-to-end delay of the routes from formulation were compared with the routes obtained by the most stable routes from DSR. The routing effects were eliminated by loading such routes as static routes. The y-axis shows the ratio of the end-to-end delay using the formulation routes to the ones used by the DSR, and the x-axis depicts the number of connections. A phenomenal improvement in the delay can be found while using the globally aware routing. Similar results were found for random scenarios also. However, the throughput improvement at higher contention was marginal, the reason being the greater packet drops while using the formulation routes. At higher contention, the substantial effect of packet drops on throughput is not captured by the above formulation. This signi es the importance of modeling the scheduling interactions. 7.6.3 MAC Modeling Accounting for the Effect of Scheduling The formulations discussed thus far ignore the effect of the scheduler; they assume that aggregate interference metrics such as busy time are the sole factor in uencing link capacity (as would be the case, for example, under an ideal scheduler). Such formulations measure the available capacity of the links (Property 1 discussed in Section 7.4.3), but do not account for the effect of imperfect CSMA-based schedulers

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Our discussions up to this point focused on improving EQUIPMENT Element performance As discussed earlier in this section, EQUIPMENT is just one of several system element performance effecters that contribute to overall system performance Measurable system performance may also be achieved by improving the PERSONNEL Element performance aspects without having to procure new EQUIPMENT solutions How do we do this EQUIPMENT system performance is often limited by system operator and maintainer skills, pro ciency, and performance System operators and maintainers require training to improve their knowledge, skills, and pro ciency in understanding the limitations of the EQUIPMENT and HOW to properly apply the EQUIPMENT The mechanisms for improving human performance includes operator and maintainer selection, classroom training, eld experience operating the system, and sometimes LUCK Experienced system operators and maintainers are often responsible for training new students.

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