My good action of the day is to have fallen in the rabbit hole of discovering the justification behind your comment.
First, it’s more queueing theory than distributed systems theory (slightly pedantic, but I’m more used to the latter, which explained my lack of knowledge of this result).
Second, even if you look through Queueing theory resources, it’s not that obvious where to look. I’ve finally found a helpful blog post which basically explains how under basic models of queues the average latency behaves like 11−load, which leads to the following graph (utilization is used instead of load, but AFAIK these are the same things):
This post and a bunch of other places mentions 80% rather than 60%, but that’s not that important for the point IMO.
One thing I wonder is how this result changes with more complex queuing models, but I don’t have the time to look into it. Maybe this pdf (which also includes the maths for the mentioned derivation) has the answer.
My good action of the day is to have fallen in the rabbit hole of discovering the justification behind your comment.
First, it’s more queueing theory than distributed systems theory (slightly pedantic, but I’m more used to the latter, which explained my lack of knowledge of this result).
Second, even if you look through Queueing theory resources, it’s not that obvious where to look. I’ve finally found a helpful blog post which basically explains how under basic models of queues the average latency behaves like 11−load, which leads to the following graph (utilization is used instead of load, but AFAIK these are the same things):
This post and a bunch of other places mentions 80% rather than 60%, but that’s not that important for the point IMO.
One thing I wonder is how this result changes with more complex queuing models, but I don’t have the time to look into it. Maybe this pdf (which also includes the maths for the mentioned derivation) has the answer.
“helpful blog post” is down, here it is on wayback