TY - GEN
T1 - Adaptive Exchange of Distributed Partial [email protected] for Highly Dynamic Systems
AU - Gotz, Sebastian
AU - Gerostathopoulos, Ilias
AU - Krikava, Filip
AU - Shahzada, Adnan
AU - Spalazzese, Romina
PY - 2015/8/12
Y1 - 2015/8/12
N2 - Future software systems will be highly dynamic. We are already experiencing, for example, 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 knowledge and conditions over the knowledge. We illustrate the feasibility of our technique by discussing its realization based on two state-of-the-art approaches.
AB - Future software systems will be highly dynamic. We are already experiencing, for example, 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 knowledge and conditions over the knowledge. We illustrate the feasibility of our technique by discussing its realization based on two state-of-the-art approaches.
KW - Cyber-Physical Systems
KW - Model synchronization
KW - [email protected]
UR - http://www.scopus.com/inward/record.url?scp=84953275230&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=84953275230&partnerID=8YFLogxK
U2 - 10.1109/SEAMS.2015.25
DO - 10.1109/SEAMS.2015.25
M3 - Conference contribution
AN - SCOPUS:84953275230
T3 - Proceedings - 10th International Symposium on Software Engineering for Adaptive and Self-Managing Systems, SEAMS 2015
SP - 64
EP - 70
BT - Proceedings - 10th International Symposium on Software Engineering for Adaptive and Self-Managing Systems, SEAMS 2015
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 10th International Symposium on Software Engineering for Adaptive and Self-Managing Systems, SEAMS 2015
Y2 - 18 May 2015 through 19 May 2015
ER -