![](bg8.png)
deployment environment needs to be considered. This
was difficult in our setup, because it would require a
flexibly configurable deployment infrastructure, that
was not available in our case.
The complexity of microservices-based applica-
tions, also demonstrated by the need to consider the
actual deployment of an application, represents a gen-
eral challenge. By limiting the experiments to singu-
lar architectural changes, introducing CPU limits to
the Kubernetes pods, and focusing on a single han-
dling at a time, influences of other parts of the appli-
cation can be minimized, but not entirely excluded.
For further experiments, these effects would have to
be further reduced or controlled for. Extending an ex-
perimental system in this way is an interesting pos-
sibility for future work. Because the experiments in
this paper focused only on throughput, future work
could also examine additional runtime metrics. An-
other category in the literature survey section that is
also promising is Reliability. Especially the fault tol-
erance of a microservices-based application is highly
dependent on its architecture and should thus be worth
to explore. The presented generation system could be
extended for this by adding the possibility to config-
ure failover strategies for communication links or run-
ning services in a replicated way. Architectural met-
rics about failover and replication could then be put in
relation to the reliability of the application.
7 CONCLUSION
To satisfy runtime quality requirements of applica-
tions, it is often necessary to make trade-offs within
the design of an application. If the architecture of
the application does not fit the requirements during
its runtime costly redevelopments might be neces-
sary. With known relations between architectural
metrics of microservices-based applications and run-
time metrics, design time evaluations would be pos-
sible. Based on a model-driven generation system
we performed multiple experiments to investigate ex-
pected relations between the two types of metrics for
a microservices-based application. These results are
able to strengthen our understanding of relations be-
tween architectural and runtime metrics. Thus, they
can be used in a more informed way in scenarios
where architectural decisions have to be made.
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