Restart cluster job on task completion

## __Use case conditions__:
- tasks have large and variable run times
- task executes 3rd party software, such that dask cannot migrate the execution state
- workers have a wall time eg HPC cluster

## __Current behavior__:
Compute is wasted on tasks that cannot finish in remaining walltime, task is restarted from scratch on new worker after worker death.

Originally working with this issue on the forums [here](https://dask.discourse.group/t/how-to-handle-job-migration-of-3rd-party-tasks/1312)

## __More context__:

Task executes a 3rd party software (mine requiring multiple threads, see the `issues` linked in the above post for examples). Code looks something like the following:

```
import dask
from dask_jobqueue import SLURMCluster
from distributed import Client
import distributed
import logging
import time

def do_one(x):
worker = distributed.get_worker()
logger = logging.getLogger('worker')
logger.setLevel(logging.INFO)
fh = logging.FileHandler(f'worker_{worker.id}.log', mode='w')
fh.setLevel(logging.INFO)
logger.addHandler(fh)

logger.info(f"I am working on {x}")
# run third party software
# this takes a while but is not very consistent in total time
logger.info(f"I finished {x}")
return f"Input {x} done"

if __name__ =='__main__':
cluster = SLURMCluster(
memory="1g",
walltime='00:30:00',
job_extra_directives=['--nodes=1', '--ntasks-per-node=1'],
cores=1,
processes=1,
worker_extra_args=["--lifetime", "28m", "--lifetime-stagger", "50s"],
job_cpu=6
)
cluster.adapt(minimum=2, maximum=10)
client = Client(cluster)

results = []
for future in distributed.as_completed(client.map(
do_one, list(range(100,132))
)):
result = future.result()
results.append(result)
```
Result of `worker_XXX.log`
```
2022-11-07-12:00:00 INFO I am working on 1
2022-11-07-12:22:00 INFO I finished 1
2022-11-07-12:22:03 INFO I am working on 10
```
Worker XXX is killed at 12:29 due to walltime. 7 minutes of compute is wasted because the state cannot be changed. Task 10 starts from scratch on a new worker.

## __Attempts to fix__:
Short of figuring out a way to move the execution state, I figure the best strategy is to have each task get a brand new SLURM job, so that no compute is wasted and any task that can finish in the walltime works.

1. I tried a worker plugin like so:
```
class KillerNannyPlugin(distributed.diagnostics.plugin.WorkerPlugin):
"""Better as a nanny plugin but those are not running transitions properly."""
def __init__(self, max_stagger_seconds: float = 5):
self.max_stagger_seconds = max_stagger_seconds

def setup(self, worker):
self.worker = worker

def transition(self, key, start, finish, *args, **kwargs):
if start == 'memory' and finish == 'released':
self.worker.io_loop.call_later(3+random.random() * self.max_stagger_seconds, self.worker.close_gracefully, restart=True)
```
- This was successful in ensuring each task got its own job, but caused task repeat to be on the order of 100%, defeating the point of saving compute

2. Have the client retire the worker that just completed a task when the job is done, like so:

```
for future in distributed.as_completed(client.map(
do_one, list(range(100,132))
)):
who_has = client.who_has(future)
closing = list(list(who_has.values())[0])
client.retire_workers(closing)
```
- I also added a small time delay to the worker function such that next tasks did not start (and begin wasting energy) while the client retired the worked.
- This seems to have the desired effect, any consequences of this are not clear to me as I observe that tasks are not repeated nor do tasks start on a job that is about to time out.

I think this should be codified somehow as the "solution" above is quite hacky. My intuition says that it would fit best as a scheduler plugin, as using the worker plugin above clearly had adverse effects on task balancing. Happy to help contribute with some input on where this would fit best if it would be a useful addition.