Deadlock allocating containers for mappers and reducers

I have a small cluster consisting of 8 desktop class systems (1 master + 7 workers).
Due to the small memory of these systems I configured yarn as follows:

yarn.nodemanager.resource.memory-mb = 2200
yarn.scheduler.minimum-allocation-mb = 250

On my client I did

mapreduce.map.memory.mb = 512
mapreduce.reduce.memory.mb = 512

Now I run a job with 27 mappers and 32 reducers.
After a while I saw this deadlock occur:

• All nodes had been filled to their maximum capacity with reducers.
• 1 Mapper was waiting for a container slot to start in.

I tried killing reducer attempts but that didn't help (new reducer attempts simply took the existing container).

Workaround:
I set this value from my job. The default value is 0.05 (= 5%)

mapreduce.job.reduce.slowstart.completedmaps = 0.99f