> On 16 May, 2021, at 11:44 pm, Michael Richardson <m...@sandelman.ca> wrote:
> 
> Your analogy is definitely the result of optimizing for batches rather than 
> latency.

I really don't know how you got there from here.  What I described is basically 
a pipeline process, not batch processing.  The delay is caused by the fact that 
the product already in the pipeline has already been bought by the hardware 
store, and thus contractually the loggers can't divert it to an individual 
customer like me.

You can think of one bag of firewood as representing a packet of data.  I've 
requested a particular number of such bags to fill my trailer.  Until my 
trailer is full, my request is not satisfied.  The hardware store is just 
taking whatever manufacturing capacity is available; their warehouse is *huge*.

We can explore the analogy further by changing some of the conditions:

1: If the felling of trees was the bottleneck of the operation, such that the 
trimming, chopping and bagging could all keep up with it, then the delay to me 
would be much less because I wouldn't have to wait for various backlogs (of 
complete trees, branches, and piles of firewood) belonging to the hardware 
store to be dealt with first.  Processing each tree doesn't take very long, 
there's just an awful lot of them in this patch of forest.

1a: If the foreman told the felling team to take a tea break when a backlog 
built up, that would have nearly the same effect.  That's what an AQM does.

2: If the hardware store wasn't involved at all, the bags of firewood would be 
waiting, ready to be sold.  I'd be done in the time it took to load the bags 
into my trailer.

3: If the loggers sold the *output* of the process to the hardware store, 
rather than having them reserve it at the head of the pipeline, then I might 
only have to wait for the throughput of of the operation to produce what I 
needed, and load it directly into my trailer.  *That* would be just-in-time 
manufacturing.

 - Jonathan Morton
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