2 Ping! - Ping! Zine Web Tech Magazine

essential; after all, it takes a human being several seconds just
to mentally process a problem.
The point of this mental exercise was to convert times into more
“tangible” measurements, which may be a surprise to those who
haven’t thought about it before. If you found that frightening, the
situation is even worse than presented above. Most of those
problems presented above are the kind that the web host has
some measure of control over. In real life, you have to worry
about datacenters, communications lines, electricity, and a variety
of other things that the host has little to no ability to manage.
Even when conditions are ideal, there will always be the
possibility of something catastrophic. All the preparation in the
world can’t totally protect a single datacenter from something
as rare and drastic as a hurricane, a terrorist attack, or a large
meteorite hitting your servers. To the individual host, those
events probably lend themselves to greater concerns than
uptime, but that isn’t going to keep your clients from taking you
to court over losses.
So what’s the moral of the story? If you seriously want
to approach 100% uptime, you’d better have redundancy,
monitoring, and automation in place, with hosting infrastructure
spread out across large areas of the country or planet.
THE PROBABILITIES OF INDEPENDENT EVENTS
Now that we’ve looked at some of the math and practical
considerations of uptime percentages, most of what we’ve
examined has been a bit of a downer. It’s time to turn the tables
and make the math start working for the good guys, the hosts.
Revisiting statistics, there is an important property in probabilities
that is going to help more to achieve huge uptimes without being
subject to the things that web hosts have no control over. That
property is the statistical fact that the probability of independent
events occurring is the product of their individual probabilities.
To translate that into English, let’s say we have two servers
with each having a 1% probability of being down. Assuming that
downtime is independent, then we multiply the probabilities of
each going down to get the odds of both being down. This works
out to 1% times 1%, or 0.01%. To describe it another way, two
servers may only be able to handle 99% uptime, but the odds
that at least one of them are up is now 99.99% (assuming these
events are independent).
Incidentally, don’t try figuring this up by multiplying the uptimes.
That leaves you with the probability of both being up and running,
but we want to calculate the odds of at least one being up.
Table 2 – Impacts of Redundancy on Uptime
Uptime %
90.0%
99.0%
99.9%
1 Server
90.0%
99.0%
99.9%
2 Servers
99.0000%
99.9900%
99.9999%
3 Servers
99.9000000%
99.9999000%
4 Servers
99.9900000000%
99.9999990000%
99.9999999% 99.9999999999%
If you examine the results of these probabilities (Table 2), there
are some striking results. While a particular server may only
provide 99.9% uptime, four independent servers working in
tandem increase that uptime to 99.9999999999%. That’s going
from three to twelve “nines,” which works out to going from 42
minutes of downtime to mere milliseconds.
The word that can’t be emphasized enough in this case is
independent. That’s the only way this works.