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[38]; for similar results obtained in the context of a uid queueing system under heavy traf c, see 5 in this volume) That is, for all m ! 1, r m satis es r m k % ck 2H 2 ; as k 3 I; 0:5 < H < 1; 20:3 where the parameter H is called the Hurst parameter and measures the degree of long-range dependence in X m ; in terms of the tail index 1 < < 2 that measures the degree of ``heavy-tailedness'' in Eq (202), H is given by H 3 =2 Intuitively, long-range dependence results in periods of sustained greater-thanaverage or lower-than-average traf c rates, irrespective of the time scale over which the rate is measured In fact, for a zero-mean covariance-stationary process, Eq (20.



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So if you are going to make a concerted effort to reach out to people who can carry your message the best, you have to identify who the In uentials really are on a given topic your topic. This is where analysis gets interesting and a little murky at the same time.

As a result, every write that makes it to ebony s disks also makes it to ivory s disks This means that if something happens to ebony, all of its data is guaranteed to be present and up-to-date on ivory For some extremely critical applications, this guarantee is worth the impact on performance For others, the impact will be too great, and different options must be considered..





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3) implies (and is implied by) asymptotic (second-order) self-similarity; that is, after appropriate rescaling, the overall traf c rate processes X m have identical second-order statistical characteristics and ``look similar'' for all suf ciently large.

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When data is replicated from one system to another, the data can be replicated so that write ordering is maintained, or so that it is not. In general, of the types that we discuss, only the last one, periodic replication, fails to maintain write ordering. When write ordering is maintained, data that is written to the master host, country, will be written to the destination host, western, in the same order it is written to country. If a single database transaction generates a series of 50 disk writes, those disk writes will hit both hosts disks in the same order. As a result, if country crashes or suffers another interruption in the middle of its replication, the data on western is incomplete (as some of the writes did not get replicated), but it is consistent. A good database can work through a situation where data is incomplete but consistent. If country s writes are sent to western in any old order, then it is likely that write number 50 will reach western before, say, write 14 does. If, for example, write 50 is a confirmation of the data in the rest of the transaction, but some of the transaction s data is missing, that s a problem. The bottom line is that if your data is not replicated in the same order in which it was originally written to country, when country goes down, the data is likely to be corrupted and unusable on western. That likelihood defeats the entire purpose of implementing replication software.

time scales m. In other words, Eq. (20.3) holds if and only if for all suf ciently large time scales m1 and m2, we have m1 H X m1 % m1 H X m2 ; 1 2 20:4

where the quality is in the sense of second-order statistical properties and where 1 2 < H < 1 denotes the self-similarity parameter and agrees with the Hurst parameter in Eq (203) The ability to explain the empirically observed self-similar nature of aggregate data traf c in terms of the statistical properties of the individual connections that make up the overall traf c rate process shows that (asymptotically) self-similar behavior (1) is an intrinsically additive property (ie, aggregate over many connections), (2) is mainly caused by user=session=connection characteristics (ie, Poisson arrivals of sessions, heavy-tailed distributions with in nite variance for the session sizes=durations), and (3) has little to do with the network (ie, the predominant protocols and end-to-end congestion control mechanisms that determine the actual ow of packets in modern data networks).

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