Model-based reinforcement learning for service mesh fault resiliency in
a web application-level
- Fanfei Meng ,
- Lalita Jagadeesa ,
- Marina Thottan
Abstract
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.