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some other form of giveback. There are so many factors that are out of the control of system vendors, and are therefore disallowed in the contracts, that those contracts seldom have any teeth, and even more seldom pay off. Compare, for instance, the potential reliability of a server located in a northern California data center where, in early 2001, rolling power blackouts were a way of life, with a server in, say, Minnesota, where the traditionally high amounts of winter snow are expected and do not traditionally impact electric utility service. Despite those geographical differences, system vendors offer the same uptime contractual guarantees in both places. A system vendor cannot reasonably be expected to guarantee the performance of a local electric power utility, wide area network provider, or the data center cooling equipment. Usually, those external factors are specifically excluded from any guarantees. The other problem with the nines is that availability is a chain, and any failed link in the chain will cause the whole chain to fail. Consider the diagram in Figure 2.1, which shows a simple representation of a user sitting at a client station and connected to a network over which he is working. If the seven components in the figure (client station, network, file server and its storage, and the application server, its application, and its storage) have 99.99 percent availability each, that does not translate to an end user seeing 99.99 percent availability. To keep the math simple, let s assume that all seven components have exactly the same level of expected availability, 99.99 percent. In reality, of course, different components have different levels of expected availability, and more complex components such as networks will often have lower levels. The other assumption is that multiple failures do not occur at the same time (although they can, of course, in real life); that would needlessly complicate the math.
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File Server Storage Network Storage App Server Figure 2.1 A user of a simple network.
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Client Station
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Availability of 99.99 percent over each of seven components yields a simple formula of 0.9999 to the seventh power, which works out to 99.93 percent. That may not sound like a huge difference, but the difference is actually quite significant:
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Availability of 99.99 percent spread over a year is about 52 minutes downtime. Availability of 99.93 percent spread over a year is over 6 hours of downtime.
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Another way to look at the math is to consider that for all practical purposes, the seven components will never be down at the same time. Since each component will be responsible for 52 minutes of downtime per year (based on 99.99 percent availability), 7 times 52 is 364 minutes, or just over 6 hours per year, or 99.93 percent. The actual path from user to servers is going to be much more complicated than the one in Figure 2.1. For example, the network cloud is made up of routers, hubs, and switches, any of which could fail and thereby lower network availability. If the storage is mirrored, then its availability will likely be higher, but the value will surely vary. The formulas also exclude many other components that could cause additional downtime if they were to fail, such as electric power or the building itself. Consider another example. Six of the seven components in the chain deliver 99.99 percent availability, but the seventh only achieves 99 percent uptime. The overall availability percentage for that chain of components will be just 98.94 percent. Great returns on investment can be achieved by improving the availability of that weakest link. So, while some single components may be able to deliver upwards of 99.99 percent availability, it is much more difficult for an entire system, from user to server, to deliver the same level. The more components there are in the chain and the more complex the chain, the lower the overall availability will be. Any bad component in the chain can lower overall availability, but there is no way for one good component to raise it above the level of the weakest link.
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