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In fact, there is a whole range of possible availability levels that range from an absolute requirement of 100 percent down to a low level, where it just doesn t matter if the computer is running or not, as in the last bullet We call this range the Availability Continuum, and it is depicted graphically in Figure 31 Every computer (in fact, every system of any kind, but let s not overextend) in the world has availability requirements that place it somewhere on the Continuum The hard part is figuring out where your system s availability requirements place it on the Continuum, and then matching those requirements to a set of protective technologies.



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Although it is best to determine the appropriate level availability that is required for each system and not change it, the reality is that over time just about all systems tend to drift higher on the Continuum In Figure 31, we chose a few different types of systems and indicated where on the Continuum they might fall It is best to determine an appropriate point on the Continuum for a critical system and leave it there, because it is simpler and more straightforward to design availability into a system when it is first deployed rather than add incremental improvements over time The incremental improvements that follow reevaluations of availability needs invariably cause additional downtime, and in many cases the enhancements are not as reliable as they might have been if they had been installed at the system s initial deployment.





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Quantity g z is de ned as long as z does not belong to the set of singularities zi . Under this restriction, the expression g z converges because the series in zi 1 converges. Indeed, zn 1 en = l 1 O e2 when n 3 I, andQ series in ei the 1 converges (see Fig. 11.6). In passing, g z a O 1=z with a I 1 lei zi , i 1 when jzj 3 I. In the following we will establish that g 1 x 1 axb O xb e when x converges to 0 with the condition that jarg x j < p y for some a and y < p=2. We de ne l x log g 1 x log a . We have 1 lei zi x x log 1 log 1 log a : zi 1 zi 1 i 1

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Systems drift higher on the Continuum over time because cost of implementation is an important aspect of determining exactly how well protected a system needs to be Less enlightened budget holders tend to give less money for needed protective measures than they should When a failure occurs that falls outside the set of failures that the system has been designed to survive, the system will go down, probably for an extended period When it becomes.

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Since l x log a o 1 when x 3 0 and since l x O 1=x when x 3 I, the Mellin transform l* s of l x is de ned on the strip 0 < R s < 1 and has expression (cf. [9])

apparent to the budget holder that this outage is costing his business a lot of money, he will likely approve additional spending to protect against future failures. When he does that, he is nudging the system up the Continuum. The higher a system needs to be on the Continuum, the more it costs to get it there. The higher cost is necessary because in order to achieve higher levels of availability, you need to protect against more varieties of more complicated and less frequently occurring outages.

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The ultimate in protection requirements!

We identify in s sin ps 1 p the Mellin transform of function log 1 x . To get asymptotics of l x when x 3 0, we identify the singularities of l* s located on the left of the de nition domain, that is, for negative R s , in order to use the residue theorem in the reverse Mellin transform. The rst encountered pole is at s 0 since (s sin ps 1 p has a double root at s 0. This pole is nally of degree 1 since the factor in front of it also has a root at PI s 0; its residue is i 1 log 1 lei zi log a . There is also another pole at s b. Indeed, s s I P 1 s 1 lei zi 1 sei 1 l s 1 O ei zi 1 zi 1 i 1 i 1

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