A Model-Driven Approach to Enable Adaptive QoS in DDS-Based Middleware

Critical and distributed systems need to be reliable and comply with the required performance at run-time. In this vein, data distribution service for real-time systems (DDS) provides developers with highly configurable middleware to control the end-to-end quality of service (QoS) of the applications through a wide range of attributes. However, dynamic and unpredictable environment pose a major challenge to these systems as their workload and resources may fluctuate significantly in time depending on the execution context. Developers usually find it difficult to choose and apply the right DDS QoS attributes, as once selected, they remain fixed during the whole execution of the system. They do not automatically change according to the execution context, e.g., to meet nonfunctional requirements related to performance or resource consumption. Moreover, changing the QoS attributes at run-time may lead to incompatibilities, since the configuration used by the different participants needs to be mutually consistent. In this paper, we propose a model-driven approach that enables the safe, automatic, and transparent adaptation of the QoS attributes in DDS-based middleware, providing the best performance possible within the available resources at run-time. An example in robotics is presented to demonstrate the feasibility and the benefits of our proposal.