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While the overall times are of interest, one expects that different protocols have different characteristics. We compute the discrepancies on a per application basis. Figure 15.10 visualizes their values in a bar plot. Again the height of the bars



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We looked at issues for desktop applications that use databases in the previous chapter. On the server side, a database application needs to be able to recover itself, often independently of the database server. Server-based applications may need to notify the users of failures that require manual intervention, so recovery requires coordination of the server-resident and desktop-resident application components. If you re using a browser-based (HTML client) front end to your application, then generation of the appropriate error and user information pages is the responsibility of the server-side application alone.

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A well-functioning application that needs to use network services is at risk of network connectivity impacting its predictable behavior. To continue functioning normally, the networked application relies on routing, remote host availability, and name service configuration. The most common error is that a host or network is unreachable, meaning that either the network is partitioned because of a routing failure or some intermediate routing information is no longer valid, so that clients on one end of the network can t find the servers on the other end. Assuming that you ve done a bare minimum of redundant network design with multiple routers and rapid detection of outages, connectivity failures reflecting network outages should be short-lived. On the other hand, without network redundancy, you need an alternative plan either

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In the same way, web marketers have attempted to divine signi cance from the rows of IP addresses, le names, byte counts, and time stamps in the log les of web servers from the very beginning. Over the years, with the advent of additional data collection technologies, we have proven that our conjectures and prognostications are valuable to business. Hypotheses can be scienti cally tested to show that we understand and can in uence onsite behavior by making speci c changes to a web site and measuring the results. We can alter our prospective customers behavior by altering our promotional efforts and persuasion techniques.

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another server for the same service or some way to stall the requesting application If you are merely waiting for the routers to reestablish end-to-end connectivity, a short wait-and-retry loop should make your application tolerant of these network hiccups A second network connectivity problem is when your application and a host undergoing failover or reboot engage in a network footrace to see if the server can start listening on a port before the client s request arrives there If the server wins, there s no visible impact; but if the client wins, it may see a port unreachable message indicating that the remote host was there but nobody was home on the desired port number Again, if you assume that the remote service is on its way back to full health, then a short timeout and retry should establish a connection on the next attempt.

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Another set of network problems has to do with connections reset by peers This is the networking equivalent of I don t like you anymore Usually, a network connection reset occurs when one end of the connection reboots, telling the other end to drop as soon as it s able to do so Resets can also be sent on connections that do not respond to any traffic for a long time, typically at the expiration of a keep-alive timer that sends periodic probes to the other end to be sure someone is still listening even if there s no traffic on the socket When a database server reboots, all of its clients see their TCP connections reset by peers, and they have to reconnect.

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Fig. 15.8 Empirical and tted cumulative interarrival time distributions of one CMU and one external AT&T dataset. ^ TABLE 15.5 The 68.26% Con dence Intervals for l2 for the Fitted Models of Some Datasets and the Parameters of the Fitted Weibull model Dataset Model Weibull Lognormal Exponential Pareto Weibull c Weibull a CMU 29 30 June 0.06 0.07 0.79 0.82 2.08 2.17 2.25 2.42 0.569 3.175 External AT&T 18 Nov. 8 Dec. 0.08 0.09 0.93 0.95 1.12 1.14 3.25 3.41 0.600 3.894 Internal AT&T 8 23 Dec. 0.18 0.20 1.48 1.58 2.34 2.53 13.6 14.9 0.419 32.582

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