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necessary to model this data. For the one hour long 1995 LBL-5 dataset, the distribution of HTTP interarrival times is modeled best using a lognormal distribution while the total dataset is explained best via an exponential or Weibull model. For the one hour long 1995 DEC-4 dataset the distribution of HTTP interarrival times is modeled best using the Weibull distribution. The overall distribution is also explained best by a Weibull distribution. Yet, with a shape parameter of 0.83, the distribution is relatively close to an exponential distribution, which is not too surprising given that most connections are either TELNET or FTP connections. Indeed, because more than one- fth of all TCP connections are HTTP connections, a distribution with a heavier tail than the exponential distribution appears appropriate. While most of the application-speci c Weibull models have small discrepancies, we observe that the discrepancies are quite large for some applications such as NNTP, POP or PROTO. We identi ed two major causes for such large discrepancies: , bimodality and timer-driven applications. Since neither of our models is able to t such distorted distributions, the resulting large discrepancy values are no surprise. 15.5.5 Application-Speci c Interarrival Times over Shorter Time Periods



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Although the traces where collected over time periods of varying length (from less than one day to three weeks), the Weibull distribution produces good models for the interarrival times of the various applications This is a rst indication that the appropriateness of the tted Weibull distribution is not adversely affected by the considered time interval Indeed, the observations regarding the goodness of the tted distribution hold true over almost all time periods that are long enough to allow suf cient observations It even holds if we select just one compute server over a one hour time period or aggregate over a number of single user machines within one hour time periods We start by considering the HTTP connection interarrival times of the external AT&T dataset from 18 November Figure 1511 shows the discrepancy measures l2 for time periods of different length For periods of one day (Fig 15.





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for incoming requests If the server can t bind to the port, clients won t be able to find it at the agreed-upon location, and to the clients, the server is still down even though the host is running and the application has restarted Guaranteeing that well-known addresses can be reused is critical for restarting applications without a complete cold start via server reboot Only two knobs need to be turned to ensure that server processes can reuse their previous network registrations First, make sure that you are specific that a socket address can be reused, by handing the SO_REUSEADDR option to a setsockopt() call (using Berkeley socket semantics) This system call indicates that even after the socket owner crashes, the socket s port number can be reused later on by another process.

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11(a)), one hour (Fig 1511(b)) and even 20 minutes (Fig 1511(c)), the Weibull distribution yields the best t If we isolate speci c traf c sources within our traces (eg, a compute server at AT&T), the Weibull distribution still gives the statistically best model Figure 1512(a) depicts the arrival time series of the individual sources as well as the total arrival time series for all HTTP connections of the external AT&T dataset from 16 to 31 March over some arbitrary one hour time period Figure 1512(b) and (c) show the empirical and tted cumulative interarrival time distribution of the interarrival time distribution of two speci c sources from the same data used for Fig 1512(a).

Again the t of the Weibull distribution is far superior to all the others an observation that is underlined by the discrepancy measure for other sources and time periods The time-dependent discrepancies for the interarrival times of another application, TELNET, are shown in Fig 1513 for the 29 30 June 1995 dataset Here the rst.

The second knob is a TCP/IP configuration control that sets the TCP keepalive interval and the close_wait interval, both of which are used to determine when a socket that has one end closed is really, totally, most sincerely dead The defaults for these values are generally in the range of several hours not exactly the timeout you want before you restart an application in an attempt to recover a service without a reboot Here s where your challenge as a high-availability system architect arises: Set the timeout too small, and heavy network congestion coupled with lossy networks (such as clients connecting over the Internet) may result in discarding sockets that are functional, but moving little data.

Fig. 15.11 Quality of t of application-speci c (HTTP) and time-dependent connection interarrival times for the external AT&T 18 November to 8 December 1995 dataset.

Set the timeout too high, and you might as well reboot or failover to another server because you ll have to wait for the entire timeout period before restarting the application that wants to reuse its previous port registration Using a lower bound of 3 minutes is fair meaning that you can restart applications after 3 minutes of a crash Any network connection that remains data-free for three minutes is suffering from other problems, and abnormally terminating a socket connection is the least of them..

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