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So, the question remains: Which configuration style is better As with so many of the questions that we examine throughout the book, the complete answer is not entirely clear-cut. Active-passive is unquestionably better for pure availability. But because of the added implementation costs, it is just as unquestionably more difficult to sell it to management. From a cost perspective, active-passive is superior. It requires fewer systems, and as a result, less space, less electricity, less cooling, and less administration. In the end, like nearly everything else we have discussed, it depends on your needs. If your applications are so critical that you need to squeeze every possible iota of availability out of them, then active-passive is the way to go. If rare outages are tolerable, then active-active clustering should be fine. If you believe that active-passive is truly the way to go, but your management is not convinced, we recommend selling it to them the same way you might sell life insurance. Clustering software and configurations are just like life insurance, after all. Although nobody likes to admit it, system outages are inevitable. When they occur, they cost money, and depending on when they occur and the importance of the system that has failed, they can cost a lot of money. The parallels to life insurance are obvious.



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19.2.3 is able to guarantee E for individual VCs while LIFO is not. Both dropping policies dropped exactly the same total number of cells. 19.4.1.2 Scenario 2: Heterogeneous Cell-Loss Proabability Requirements Consider two different classes of traf c with desired upper bounds on cell-loss probabilities, E1 and E2, respectively. Multiple VCs with same delay-jitter bound (frame size) and cell-loss probability requirement are considered part of the same class. Let fXi xi i 1; 2 be the density function for the number of bits=frame transmitted by class i. The following two methods are upper-bound estimates, with progressively tighter bounds and increased computational complexity. The algorithm below generalizes to more than two classes. Alternative 1 The simplest upper bound on equivalent bandwidth is the sum of equivalent bandwidths for each Ei i 1; 2 , computed in isolation. The minimum bandwidth required for class i is the 1 E th quantile of Xi , that is, the smallest Ci that satis es I





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Every few months I send my life insurance company a check. In return for that timely payment, I receive . . . nothing! (Not even a lousy calendar.) And personally, I hope that relationship continues for a very long time. If I were to stop paying those bills, the insurance would go away, and when my family finally needs it, they won t get any of the benefits. (Personal note to my wife: Relax, I have no intention of stopping my life insurance payments.) If your company chooses not to invest in a standby server, then when it needs it, it won t get the benefits that a reliable standby server can provide. Just as I (at least in theory) eat right and exercise so that I will live a long, healthy life, and therefore get to keep making those insurance payments and keep getting nothing in return, we build our computers with spare NICs, mirrored disks, mature applications, and all the other stuff we have discussed. Someday, despite my best efforts (and although I really don t like to think about it), that life insurance policy will finally pay out. And despite your best efforts, someday that critical server is going to fail. When it does, you ll want the most reliable server backing it up. Otherwise, downtime will be the inevitable result. The most reliable takeover server is one that isn t doing anything, except waiting to step in. Evan

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19:8

However, more than four out of every five two-node clustered configurations that we have been involved with over the years have been active-active configurations. Realistically, they are more economical, and the risks that they bring along are acceptable for most environments. Once again, your mileage may vary. Choose the configuration that makes the most sense for you.

P and the equivalent bandwidth is C i Ci . This approach does not consider the statistical multiplexing gains across different classes and overestimates the true equivalent bandwidth needed. The following algorithm gives a tighter upper bound. Alternative 2 Let fX1 ;X2 x1 ; x2 be the joint density of fX1 x1 and fX2 x2 . We assume that traf c classes are spatially independent, that is, fX1 ;X2 x1 ; x2 fX1 x1 fX2 x2 . An algorithm for computing a tighter equivalent bandwidth estimate as follows. If there are K classes, the bandwidth needed for class k at iteration n is computed as follows (compare this to Eq. (19.9)): ! Xi ! 1 Ek 19:13

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The second term in Eq. (19.13) considers only the case when the total arrivals of all connections in a frame is less than the total capacity. It is possible to not drop any cell from connection k, even if the total number of arrivals is greater than the total capacity. Therefore, the equivalent bandwidth computation above is an upper bound.

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