![](bg8.png)
0 200 400 600 800 1000 1200
0 20 40 60 80
seconds
RAM usage in MB
0 200 400 600 800 1000 1200
0.0 0.1 0.2 0.3 0.4
seconds
CPU usage in %
Figure 3: Nagare Media Engine CPU and memory usage.
installations. One can also observe that memory us-
age does not significantly decrease after all resources
are reconciled. This is because the resources are still
kept in the Kubebuilder cache.
7 CONCLUSION
In this paper, we discussed how we used Kubernetes
as NBMP MPE in our workflow manager implemen-
tation Nagare Media Engine. Building upon the
Kubernetes platform allows for a resilient NBMP sys-
tem that can handle an eventual consistency model.
However, this model might pose additional challenges
for NBMP function developers. In an evaluation, we
demonstrated how our workflow manager can scale to
many concurrently executing workflows while being
resource efficient at the same time.
Work on Nagare Media Engine is still ongoing.
It is our plan to implement a library of compati-
ble NBMP functions for efficient and reliable NBMP
workflows. As the second edition of the NBMP stan-
dard is currently in work, we are eager to see how this
technology will evolve in the future.
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