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Another memory trick to calculate the allowed downtime duration for an "-nines" availability percentage is to use the formula seconds per day. For example, 90% ("one nine") yields the exponent 4 − 1 = 3 {\displaystyle 4-1=3} , and therefore the allowed downtime is 8.64 × 10 3 {\displaystyle 8.64\times 10^{3}} seconds per day.
In this context, a "one nine" (90%) uptime indicates a system that is available 90% of the time or, as is more commonly described, unavailable 10% of the time – about 72 hours per month. [8] A "five nines" (99.999%) uptime describes a system that is unavailable for at most 26 seconds per month. [8]
Uptime is a measure of system reliability, expressed as the period of time a machine, typically a computer, has been continuously working and available. Uptime is the opposite of downtime. Htop adds an exclamation mark when uptime is longer than 100 days.
Continuous availability is an approach to computer system and application design that protects users against downtime, whatever the cause and ensures that users remain connected to their documents, data files and business applications.
A weaker three-sigma rule can be derived from Chebyshev's inequality, stating that even for non-normally distributed variables, at least 88.8% of cases should fall within properly calculated three-sigma intervals. For unimodal distributions, the probability of being within the interval is at least 95% by the Vysochanskij–Petunin inequality ...
First order LTI systems are characterized by the differential equation + = where τ represents the exponential decay constant and V is a function of time t = (). The right-hand side is the forcing function f(t) describing an external driving function of time, which can be regarded as the system input, to which V(t) is the response, or system output.
The confidence interval can be expressed in terms of a long-run frequency in repeated samples (or in resampling): "Were this procedure to be repeated on numerous samples, the proportion of calculated 95% confidence intervals that encompassed the true value of the population parameter would tend toward 95%." [19]
A common way to do this is to state the binomial proportion confidence interval, often calculated using a Wilson score interval. Confidence intervals for sensitivity and specificity can be calculated, giving the range of values within which the correct value lies at a given confidence level (e.g., 95%). [26]