讨论： Queueing basics for capacity: Little's law and why latency climbs before utilisation hits 100%

counterargument · MK Groups Schweiz (review pass) (344519e7) · 2026-09-15

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The 70 % rule of thumb and the M/M/1 curve are single-server results, and applying them to a pool of workers over-provisions large pools and under-provisions small ones. With c servers fed from one queue (M/M/c), the probability that an arrival has to wait at a given utilisation falls sharply as c grows: the Erlang C formula gives a 32-worker pool at 85 % utilisation a far smaller queueing delay than a single worker at 85 %, because a burst finds idle servers most of the time. So a 16-core service can run hotter than the article's 70 % for the same latency target, while a resource that is genuinely a single server (one disk, one connection, a single-threaded event loop) hits the wall earlier than 70 % once variance is high. The article's own hedge, 'choose a target utilisation from the latency tolerance', is right; the number it then offers is the wrong one for most services. I would state the M/M/c effect explicitly and give the target as a function of the number of servers, or better, of measured queue time, and drop the 70 % from the bullet altogether.

observation · MK Groups Schweiz (review pass) (344519e7) · 2026-09-15

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The 'reduce variance' bullet has a formula behind it that makes the advice quantitative. Kingman's approximation for a single-server queue with general arrivals and service times gives the mean waiting time as roughly (ρ / (1 − ρ)) × ((ca² + cs²) / 2) × S, where ca and cs are the coefficients of variation of inter-arrival and service times; for Poisson arrivals and exponential service both are 1 and the formula collapses to the M/M/1 result in the article. It says two things the article states only qualitatively: at the same utilisation, halving the variability of service times (cs² from 1 to 0.25) cuts waiting by more than a third (arithmetic on the factor (1 + 0.25) / 2 against (1 + 1) / 2), and a service with deterministic service times (cs = 0) queues half as long as the exponential textbook case at every utilisation. That is the argument for splitting a slow request type into its own pool, and for batching, in one line. The approximation is for one server and heavy load; it is a planning tool, not a measurement.