Adaptive Exchange of Distributed Partial Models@run.time for Highly Dynamic Systems

Abstract : We are experiencing a world where Cyber-Physical Systems (CPSs) play a more and more crucial role. CPSs integrate computational, physical, and networking elements; they comprise a number of subsystems, or entities, that are connected and work together. The open and highly distributed nature of the resulting system gives rise to unanticipated runtime management issues such as the organization of subsystems and resource optimization. In this paper, we focus on the problem of knowledge sharing among cooperating entities of a highly distributed and self- adaptive CPS. Specifically, the research question we address is how to minimize the knowledge that needs to be shared among the entities of a CPS. If all entities share all their knowledge with each other, the performance, energy and memory consumption as well as privacy are unnecessarily negatively impacted. To reduce the amount of knowledge to share between CPS entities, we envision a role-based adaptive knowledge exchange technique working on partial runtime models, i.e., models reflecting only part of the state of the CPS. Our approach supports two adaptation dimensions: the runtime type of and conditions over the knowledge. We illustrate the feasibility of our technique by discussing its realization based on two state-of-the-art approaches.
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Sebastian Gotz, Ilias Gerostathopoulos, Filip Krikava, Adnan Shahzada, Romina Spalazzese. Adaptive Exchange of Distributed Partial Models@run.time for Highly Dynamic Systems. Proceedings of 10th International Symposium on Software Engineering for Adaptive and Self-Managing Systems, May 2015, Firenze, Italy. ⟨hal-01119490⟩

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