MTBF, FIT, and AFR

MTBF, FIT, and AFR are closely related concepts that describe component reliability. In brief,

$\text{MTBF}\propto \frac{1}{\text{FIT}}\propto \frac{1}{\text{AFR}}$

MTBF

Mean Time Between Failure (MTBF) refers to the mean time between failures of a single component or a system of components. Pedantically, a failure is something which requires replacement (like a GPU burnt up). Its units are time (hours, days, months, years) and is calculated simply as:

$\text{MTBF}=\frac{\text{time spent observing}}{\text{\# observed failures}}$

If you had to send five nodes back for replacement in the past six months, your MTBF is

$\text{MTBF}=\frac{6\text{ months}}{5\text{ failures}}=1.2\text{ months}$

If you know the survival function of a component $R(t)$, you can also express MTBF in terms of that:

$\text{MTBF}={\int }_0^{\infty }R(t)dt$

FIT

FIT (failures in time) rate is a closely related concept and is the inverse of the MTBF per billion hours in service:

$\text{FIT rate}=\frac{10^{9}hours}{{\text{MTBF}}_{\text{hours}}}$

It is a unitless quantity because it represents a number of failures.

Hardware vendors often express component reliability in terms of their FIT rate.

Failure rate ($\lambda$)

Failure rate is the frequency with which a component fails and is measured in units of failures per unit time. If the unit time is $10^9$ hours, it is the same as the FIT rate.

As with FIT rate, it is inversely related to MTBF:

$\lambda =\frac{1}{\text{MTBF}}$

Survival function

Survival function (or reliability function) describes the probability that a component will survive for at least a certain amount of time. It can be inferred if you know the failure rate $\lambda$ from above.

Survival function $R(t)=e^{-\lambda t}$

Recall that the failure rate $\lambda$ is inversely related to MTBF, you then get:

$R(t)=e^{-\frac{t}{\text{MTBF}}}$

Or maybe more meaningfully, the probability that a component will fail within a certain amount of time:

$F(t)=1-e^{-\frac{t}{\text{MTBF}}}$

AFR

AFR (annualized failure rate) seems to have two definitions:

1. The intuitive one, which is the failure rate $\lambda$ normalized to a year
2. The Wikipedia definition which is the probability that a component will fail in a year

The big difference is that #1 can be above 100% (more than one component fails per year, or a component fails multiple times per year), but #2 approaches 100% asymptotically.

Intuitive (failures per year)

The intuitive AFR, or the frequency of component failure per year, is easy to define assuming a year is 8,766 hours:

$\text{AFR}=\frac{8766\text{ hours}}{{\text{MTBF}}_{\text{hours}}}$

It is unitless because it is a percentage.

Wikipedia (probability of failure)

The Wikipedia definition is really just the survival function from above. Recall:

$F(t)=1-e^{-\frac{t}{\text{MTBF}}}$

If you use $t=8766$ hours per year, you get

$\text{AFR}=1-e^{-\frac{8766\text{ hours}}{{\text{MTBF}}_{\text{hours}}}}$

Further confusion

It doesn’t help that $\frac{1}{x}$ is often used as an approximation of $1-e^{-\frac{1}{x}}$. This results in some sources claiming that the intuitive definition is just an approximation of the other definition, but this is not true.

Predicting MTBF, FIT, and AFR

A nice property of MTBF and FIT is that, for a system of components connected in series, you can add up FIT/MTBF rates for all components to get a FIT/MTBF for the total system.

Example: Calculating node reliability

Let’s say you have a node that is comprised of the following parts (FIT values made up by ChatGPT):

Component	Component FIT ($C_i$)	Qty per node ($N_i$)	Total FIT
CPU + DRAM	1,000	2	2,000
GPU + HBM	1,500	8	12,000
NIC	300	8	2,400
Transceiver	100	8	800
BMC	500	1	500
SSD	1,000	8	8,000
SSD backplane	200	1	200
Power supply	2,000	8	16,000

The FIT for the whole node is the sum of all “Total FIT” values which is just the sum of FIT rates for every component:

$\text{Node FIT}={\sum }_i^{\text{components}}{N}_i{C}_i=(1000\cdot 2)+(1500\cdot 8)+...=41,900$

This is valid because every component is connected in series; the failure of one component causes the whole node to fail.

Warning

The above is not true if components are redundant; for example, the above assumes all eight power supplies are active/active with no redundancy. This is never true in practice; you would calculate an aggregate FIT for 6+2 power supplies and use that above.

In the above example, the node FIT is 41,900. You can then calculate:

$\text{AFR}=\frac{\text{FIT}\times 8766}{10^9}=36.7\%$

and

$\text{MTBF}=\frac{1}{\text{AFR}}=\frac{1\text{ year}}{0.367}=2.72\text{ years}=23,800\text{ hours}$

Example: Calculating cluster MTBF

Let’s say you have a cluster of 1,024 of these nodes. Calculating the FIT rate of the whole cluster is as simple as connecting all nodes in series:

$\text{Cluster AFR}=\text{\# nodes}\times \text{Node FIT}=1024\times 41,900=42,905,600$

This means

$\text{AFR}=\frac{\text{FIT}\times 8766}{10^9}=37,611\%$

Or conversely, the cluster will fail 376 times every year on average. This is better expressed as MTBF:

$\text{MTBF}=\frac{1}{\text{AFR}}=\frac{1\text{ year}}{376.11}=0.002659\text{ years}=23.3\text{ hours}$

Relationship to JMTTI

Assuming an MPI job runs across all these nodes ($\therefore$ one node failing = whole job failing), you can claim that the JMTTI would be 23.3 hours as well. However, there are more things that can interrupt a job than component failures—link flaps, kernel panics, cosmic rays, and the like. This will almost always make JMTTI lower than MTBF.

In practice

There’s a table in In practice that contains a few MTBF measurements from large-scale supercomputers.