TY - GEN
T1 - Predictive offloading in mobile-fog-cloud enabled cyber-manufacturing systems
AU - Chen, Xiaoyu
AU - Wang, Lening
AU - Wang, Canran
AU - Jin, Ran
N1 - Publisher Copyright:
© 2018 IEEE.
PY - 2018/6/15
Y1 - 2018/6/15
N2 - An industrial cyber-physical system (ICPS) integrates the physical processes, systems and networks with the computation resources to provide reliable and responsive computation services. A cyber-manufacturing system (CMS), which is generated from ICPS, poses significant challenges on reliability, accuracy and responsiveness of computation services for manufacturing decision making. In this paper, we focus on reliability and responsiveness. Due to the heterogeneities of the computation and communication capacities and conditions, demanding computation services may not be completed in a timely manner. To facilitate reliable and responsive computation services, we propose a deadline constrained predictive offloading method based on a mobile-fog-cloud (MFC) network. This method optimizes the offloading decisions by solving a quadratically constrained integer linear programming constrained by latency requirements and predicted availability of devices. A newly constructed hybrid cyber-additive manufacturing network is used to test the performance of the proposed predictive offloading method and the MFC network. The results show that the proposed method outperforms mobile computing, fog computing, cloud computing, and fog-cloud computing benchmarks to minimize resource consumption and comply with latency requirements.
AB - An industrial cyber-physical system (ICPS) integrates the physical processes, systems and networks with the computation resources to provide reliable and responsive computation services. A cyber-manufacturing system (CMS), which is generated from ICPS, poses significant challenges on reliability, accuracy and responsiveness of computation services for manufacturing decision making. In this paper, we focus on reliability and responsiveness. Due to the heterogeneities of the computation and communication capacities and conditions, demanding computation services may not be completed in a timely manner. To facilitate reliable and responsive computation services, we propose a deadline constrained predictive offloading method based on a mobile-fog-cloud (MFC) network. This method optimizes the offloading decisions by solving a quadratically constrained integer linear programming constrained by latency requirements and predicted availability of devices. A newly constructed hybrid cyber-additive manufacturing network is used to test the performance of the proposed predictive offloading method and the MFC network. The results show that the proposed method outperforms mobile computing, fog computing, cloud computing, and fog-cloud computing benchmarks to minimize resource consumption and comply with latency requirements.
KW - Cloud computing
KW - Computation offloading
KW - Fog computing
KW - Industrial cyber-physical systems
KW - Smart manufacturing
UR - https://www.scopus.com/pages/publications/85050083082
U2 - 10.1109/ICPHYS.2018.8387654
DO - 10.1109/ICPHYS.2018.8387654
M3 - Conference contribution
AN - SCOPUS:85050083082
T3 - Proceedings - 2018 IEEE Industrial Cyber-Physical Systems, ICPS 2018
SP - 167
EP - 172
BT - Proceedings - 2018 IEEE Industrial Cyber-Physical Systems, ICPS 2018
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 1st IEEE International Conference on Industrial Cyber-Physical Systems, ICPS 2018
Y2 - 15 May 2018 through 18 May 2018
ER -