Cloud manufacturing is a paradigm that represents distributed cyber-physical systems in which software abstractions and services work in strict connection to manufacturing equipment and machines. In cloud manufacturing, the cloud platform receives high-level tasks that are decomposed into subtasks, which are fulfilled by appropriate services. Despite different approaches have been proposed to compose services in order to accomplish manufacturing tasks, physical constraints have not been considered.In this paper, we propose an approach that enacts a trade-off between quality of service and physical constraints of manufacturing services in order to adapt to equipment constraints. To show the effectiveness of the proposed approach, we quantitatively compare it with a straightforward approach which does not consider any physical constraint. Experimental results of our algorithm show that the trade-off between quality of service and capacity of physical manufacturing equipment is acceptable for efficient cloud service compositions.
QoS evaluation of constrained cloud manufacturing service composition / Salierno, G.; Cabri, G.; Leonardi, L.. - 2019-:(2019), pp. 170-174. (Intervento presentato al convegno 2019 IEEE International Conference on Systems, Man and Cybernetics, SMC 2019 tenutosi a Bari, Italy nel 2019) [10.1109/SMC.2019.8913971].
QoS evaluation of constrained cloud manufacturing service composition
Salierno G.;Cabri G.;Leonardi L.
2019
Abstract
Cloud manufacturing is a paradigm that represents distributed cyber-physical systems in which software abstractions and services work in strict connection to manufacturing equipment and machines. In cloud manufacturing, the cloud platform receives high-level tasks that are decomposed into subtasks, which are fulfilled by appropriate services. Despite different approaches have been proposed to compose services in order to accomplish manufacturing tasks, physical constraints have not been considered.In this paper, we propose an approach that enacts a trade-off between quality of service and physical constraints of manufacturing services in order to adapt to equipment constraints. To show the effectiveness of the proposed approach, we quantitatively compare it with a straightforward approach which does not consider any physical constraint. Experimental results of our algorithm show that the trade-off between quality of service and capacity of physical manufacturing equipment is acceptable for efficient cloud service compositions.
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