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One of the more common topologies of FDDI is the counterrotating ring Here two classes of station may be de ned: dual (attachment) and single (attachment) FDDI trunk rings may be composed only of dual-attachment stations which have two PMD (physical layer medium dependent) entities (and associated PHY entities) to accommodate the dual ring Concentrators provide additional PMD entities beyond those required for their own attachment to the FDDI network, for the attachment of single-attachment stations which have only one PMD and thus cannot directly attach to the FDDI trunk ring A dual-attachment station, or one-half of it, may be substituted for a single-attachment station in attaching to a concentrator The FDDI network consists of all attached stations The example illustrated in Figure 1118 shows the concept of multiple physical connections used to create logical rings.

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As shown in the gure, the logical sequence of MAC connections is stations, 1, 3, 5, 8, 9, 10, and 11 Station 2, 3, 4, and 6 form an FDDI trunk ring Stations 1, 5, 7, 10, and 11 are attached to the ring by lobes branching out from stations that form it Stations 8 and 9, in turn, are attached by lobes branching out from station 7 Stations 2, 4, 6, and 7 are concentrators, serving as the means of attaching MAC entities and station functionality The concentrator examples of Figure 1118 do not show any MACs, although their presence is implied by the designation of these concentrators as stations Symbol set A symbol is the smallest signaling element used by the FDDI MAC Symbols can be used to convey three types of information:.

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This chapter has presented our overview of currently available plug-ins and add-ons that you may want to include in your MythTV system build. In subsequent chapters, we give more thorough treatment to what we believe are the most useful and popular MythTV plug-ins.

Figure 9.16. Distribution of prediction error for testing data (width is 120).

fact that although very popular documents tend to remain popular, their relative ranking does change over time However, as we choose more documents, we may also include some less popular documents Since these documents are less likely to remain popular over time, the temporal overlap decreases This phenomenon was also observed at a popular news site for wireline clients [26] 3 We observe that the document overlap between pairs of weekdays is generally higher than the overlap between a weekend day and a weekday; the overlap between two weekend days is even higher This observation is consistent with our intuition, and suggests that we should use past weekday (weekend) workload to predict future weekday (weekend) workload We also examine the requests coming from wireless users only, and observe similar results As before, the set of popular queries remains stable over time.

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The stability is especially high when we consider the most popular queries In addition, there is a signi cant difference between the access pattern on weekdays versus that on weekends 5424 Spatial Locality Finally we consider the following question: Do people in the same geographical region tend to issue a similar set of queries We take the following approach to carry out this analysis We de ne a browse request to be locally shared if at least two users in the same cluster access the same document (URL and parameter combination) We compare the degree of sharing using geographic clustering and four random clusterings In the geographic clustering case, clients in the same city are clustered together In the random clustering case, clients are clustered randomly and the cluster size is the same as in geographic clustering.

We obtained the geographic location of users using a registration database which contains zip code information for each user The zip code information is not clean 14% of users supplied zip codes that were not ve digits, which we lter out before our analysis In the remaining entries, it is still possible to have zip codes that do not match the actual user location, but the fraction is likely to be small Figure 516 compares the fraction of documents that are shared within a geographic cluster and within four random clusters for all the users The gure shows that the curve for the geographic clusters overlaps with those for random clusters This overlap indicates that the degree of sharing between geographic clustering and random clustering is comparable, and the correlation between users interest in browsing over wireless networks and their geographic location is weak.

information I (X; Y ) = H (X) H (X|Y ) =

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