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A Model-Driven Approach to Enable Adaptive QoS in DDS-Based Middleware
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Date : 2017-07-07
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Abstract—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 amajor 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.
 
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