LoadBalancedView bloats memory - bug or wrong settings?

This issue may be related to https://github.com/ipython/ipyparallel/issues/207 which is also not marked as solved, yet.
Also I posted this problem on stackoverflow (https://stackoverflow.com/questions/45781545/ipyparallels-loadbalancedview-bloats-memory-how-can-i-avoid-that).

I want to execute multiple tasks in parallel using python and ipyparallel in a jupyter notebook and using 4 local engines by executing `ipcluster start` in a local console.
Besides that one can also use `DirectView`, I use `LoadBalancedView` to map a set of tasks. Each task takes around 0.2 seconds (can vary though) and each task does a MySQL query where it loads some data and then processes it.

Working with ~45000 tasks works fine, however, my memory grows really high. This is actually bad because I want to run another experiment with over 660000 tasks which I can't run anymore because it bloats up my memory limit of 16 GB and then the memory swapping on my local drive starts. However, when using the `DirectView` my memory grows relatively small and is never full. But I actually need `LoadBalancedView`.

Even when running a minimal working example without database query this happens (see below).

I am not perfectly familiar with the ipyparallel library but I've read something about logs and caches that the ipcontroler does which may cause this. I am still not sure if it is a bug or if I can change some settings to avoid my problem.

### Running a MWE

For my Python 3.5.3 environment running on Windows 10 I use the following (recent) packages:

- ipython 6.1.0
- ipython_genutils 6.1.0
- ipyparallel 6.0.2
- jupyter 1.0.0
- jupyter_client 4.4.0
- jupyter_console 5.0.0
- jupyter_core 4.2.0

I would like the following example to work for `LoadBalancedView` **without the immense memory growth** (if possible at all):

- Start `ipcluster start` on a console
- Run a jupyter notebook with the following three cells:

<1st cell>
import ipyparallel as ipp
rc = ipp.Client()
lview = rc.load_balanced_view()

<2nd cell>
%%px --local
import time

<3rd cell>
def sleep_here(i):
time.sleep(0.2)
return 42

amr = lview.map_async(sleep_here, range(660000))
amr.wait_interactive()