The BinPackIter accounted for node reservations twice when scoring nodes
which could bias scores toward nodes with reservations.
Pseudo-code for previous algorithm:
```
proposed = reservedResources + sum(allocsResources)
available = nodeResources - reservedResources
score = 1 - (proposed / available)
```
The node's reserved resources are added to the total resources used by
allocations, and then the node's reserved resources are later
substracted from the node's overall resources.
The new algorithm is:
```
proposed = sum(allocResources)
available = nodeResources - reservedResources
score = 1 - (proposed / available)
```
The node's reserved resources are no longer added to the total resources
used by allocations.
My guess as to how this bug happened is that the resource utilization
variable (`util`) is calculated and returned by the `AllocsFit` function
which needs to take reserved resources into account as a basic
feasibility check.
To avoid re-calculating alloc resource usage (because there may be a
large number of allocs), we reused `util` in the `ScoreFit` function.
`ScoreFit` properly accounts for reserved resources by subtracting them
from the node's overall resources. However since `util` _also_ took
reserved resources into account the score would be incorrect.
Prior to the fix the added test output:
```
Node: reserved Score: 1.0000
Node: reserved2 Score: 1.0000
Node: no-reserved Score: 0.9741
```
The scores being 1.0 for *both* nodes with reserved resources is a good
hint something is wrong as they should receive different scores. Upon
further inspection the double accounting of reserved resources caused
their scores to be >1.0 and clamped.
After the fix the added test outputs:
```
Node: no-reserved Score: 0.9741
Node: reserved Score: 0.9480
Node: reserved2 Score: 0.8717
```
Also includes unit tests for binpacker and preemption.
The tests verify that network resources specified at the
task group level are properly accounted for
Also changes the logic for score when there is more than one task
requesting a device. Since inter task affinities are already normalized,
we take the average of the scores across tasks.
