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System vendors such as Sun Microsystems are constantly issuing operating system and application patches Some patches are revised and upgraded 50 or more times each as problems are fixed that depend on previous fixes Usually, it s not 50 fixes for the same bug, but 50 (or many more) fixes for different problems that grow in complexity over time, and all change the same kernel modules, library routines, or other system software components It is almost impossible to keep up with all of them On the other hand, patches are the best way to get up-to-date fixes to bugs and other problems that inevitably occur between product releases Microsoft s strategy is to release only the most critical fixes as patches, and to roll other fixes into Service Packs, which are released only occasionally.



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Since both on and off intervals have impact on transient loss performance, such a series must contain at least two on intervals and one off interval between them, as shown in Fig 131 Now let us consider such an on off on series Without loss of generality, the two on intervals in the series are indexed by n 1 and 2 Since loss behavior of the queueing system during the rst on interval in the series cannot capture the impact of the off interval, we examine loss behavior of the system in the second on interval..





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This method makes it easier to keep up on the fixes that the vendor has released; however, it can significantly delay receipt of a fix or introduce new problems in legal agreements Microsoft has been known to include new features and changes to their End User License Agreement in their Service Pack releases Managing patch revision levels and patch interactions is a challenge when vendors distribute them individually Consider all the permutations of patches that may be installed on a particular system Let s say there are just 10 patches available for a particular operating system, and each patch has three versions.

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Before the on off on series begins, the history of the system is summarized by W1 w1 Due to the renewal nature of two-state uids, as time evolves, the on off on series will probabilistically duplicate itself; that is, this traf c pattern will appear repeatedly with lengths of on and off intervals drawn from the same distributions At the beginning of each such duplication, there is an amount of traf c w1 left in the buffer, re ecting the impact of the history of the system Therefore, in order to investigate transient loss performance of the queueing system, it is suf cient to consider PflossjR n r1 g or E ljR n r1 only for n 2 and examine the behavior of the loss measures as w1 varies between 0 and B.

Ideally, you want to be on the latest version of whatever patches you have chosen to install, unless other considerations prevent you from upgrading (an application may break with a later revision of a patch, or regression testing or end-of-month configuration freezes may prevent system changes for a few weeks) Each patch has four states: It can either not be installed at all, or installed in one of the three versions Think of the 10 patches as digits in a base-4 (for the four states) numeral Enumerating every possible value of every patch in every state yields a total of 410 (or just over a million) possible permutations It is unlikely that your vendor has tested every one of these combinations to ensure interoperability.

In fact, by focusing only on this simple traf c pattern, we can observe any possible transient loss behavior of the queueing system Therefore, we use the transient loss measures for n 2, that is, PflossjR 2 r1 g and E ljR 2 r1 , to de ne two performance functions, denoted respectively by P and E The two functions depend on w1 , so we express P and E as functions of w1 For convenience of exposition, w1 is simply denoted by w in P and E That is, P w PflossjR 2 r1 g and E w E ljR 2 r1 : The above arguments are based on the fact that a two-state stochastic uid can be modeled by an alternating renewal process Essentially, the behavior of a renewal process is probabilistically periodic in the sense that interarrival times between renewals are independent and identically distributed.

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