  Microservice-based architectures enable different aspects of web applications
to be created and updated independently, even after deployment. Associated
technologies such as service mesh provide application-level fault resilience
through attribute configurations that govern the behavior of request-response
service -- and the interactions among them -- in the presence of failures.
While this provides tremendous flexibility, the configured values of these
attributes -- and the relationships among them -- can significantly affect the
performance and fault resilience of the overall application. Furthermore, it is
impossible to determine the best and worst combinations of attribute values
with respect to fault resiliency via testing, due to the complexities of the
underlying distributed system and the many possible attribute value
combinations. In this paper, we present a model-based reinforcement learning
workflow towards service mesh fault resiliency. Our approach enables the
prediction of the most significant fault resilience behaviors at a web
application-level, scratching from single service to aggregated multi-service
management with efficient agent collaborations.
