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When viewed over large enough time scales, the number of packets or bytes per time unit collected off a link in a network originates from all those connections that were active during the measurement period, utilized this link, and actively generated traf c during this time. In other words, if for ``time scales'' or ``levels of resolution'' m ) 1, X m X m k : k ! 0 denotes the overall traf c rate process, that is, the



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Within both categories, each type has its distinct advantages and disadvantages, and we spend this section discussing them in some detail. In some cases we mention specific vendors who offer products in each area. The two replication categories are independent of each other. In other words, each initiatorbased type of replication can create any amount of latency.

total number of packets or bytes per time unit (measured at time scale m) generated by all connections, then we can write X m k P Xi m k ; k ! 0; 20:1

The four types of replication, relative to the latency that they introduce, are: (1) synchronous, (2) asynchronous, (3), semi-synchronous, and (4) periodic, or





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where the sum is over all connections i that are active at time k and where Xi m Xi m k : k ! 0 represents the total number of packets or bytes per time unit (again measured at time scale m) generated by connection i.1 Thus, Eq. (20.1) captures the additive nature of aggregate network traf c by expressing the overall traf c rate process X m as a superposition of the traf c rate processes Xi m of the individual connections. Assuming for simplicity that the individual traf c rate processes Xi m are independent from one another and identically distributed, then under weak regularity conditions on the marginal distribution of the Xi m (including, e.g., the existence of second moments), Eq. (20.1) guarantees that the overall traf c rate process (or its deviations from its mean) exhibits Gaussian marginals, as soon as the traf c is generated by a suf ciently large number of individual connections. 20.2.2 Self-Similarity Through Heavy-Tailed Connections

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Focusing on the temporal dynamics of the individual traf c rate processes Xi m , suppose for simplicity that connection i sends packets or bytes at a constant rate (say, rate 1) for some time (the ``active'' or ``on'' period) and does not send any packets or bytes during the ``idle'' or ``off'' period; we will return to the challenging problem of allowing for more realistic ``within-connection'' packet dynamics in Section 20.3. For example, in a LAN environment, a connection corresponds to an individual hostto-host or source destination pair and the corresponding traf c patterns have been shown in Willinger et al. [38] to conform to an alternating renewal process where the successive pairs of on and off periods de ne the inter-renewal intervals. On the other hand, in the context of wide-area networks or WANs such as the Internet, we associate individual connections with ``sessions,'' where a session starts at some random point in time, generates packets or bytes at a constant rate (say, rate 1) during the lifetime of the connection, and then stops transmitting packets or bytes. Here a session can be an FTP appplication, a TELNET connection, a Web session, sending email, reading Network News, and so on, or any imaginable combination thereof. In fact, over 1 to 1 hour periods, session arrivals on Internet links have been shown to 2 be consistent with a homogeneous Poisson process; for example, see Paxson and Floyd [25] for FTP and TELNET sessions, and see Feldmann et al. [12] for Web sessions. Note that in the present setting, only global connection characteristics (e.g., session arrivals, lifetimes of sessions, durations of the on=off periods) play a role, while the details of how the packets arrive within a connection or within an on

batch-style, replication. The impact on both network and system performance decreases as you move from synchronous to batch style, but in return, the data on the destination system is more and more out-of-date.

Note that the processes X m and Xi m are de ned by averaging X and Xi over nonoverlapping blocks of size m.

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