摘要
With the dawning of the Internet of Everything(IoE) era, more and more novel applications are being deployed. However, resource constrained devices cannot fulfill the resource-requirements of these applications. This paper investigates the computation offloading problem of the coexistence and synergy between fog computing and cloud computing in IoE by jointly optimizing the offloading decisions, the allocation of computation resource and transmit power. Specifically, we propose an energy-efficient computation offloading and resource allocation(ECORA) scheme to minimize the system cost. The simulation results verify the proposed scheme can effectively decrease the system cost by up to 50% compared with the existing schemes, especially for the scenario that the computation resource of fog computing is relatively small or the number of devices increases.
With the dawning of the Internet of Everything(IoE) era, more and more novel applications are being deployed. However, resource constrained devices cannot fulfill the resource-requirements of these applications. This paper investigates the computation offloading problem of the coexistence and synergy between fog computing and cloud computing in IoE by jointly optimizing the offloading decisions, the allocation of computation resource and transmit power. Specifically, we propose an energy-efficient computation offloading and resource allocation(ECORA) scheme to minimize the system cost. The simulation results verify the proposed scheme can effectively decrease the system cost by up to 50% compared with the existing schemes, especially for the scenario that the computation resource of fog computing is relatively small or the number of devices increases.
基金
supported by the Fundamental Research Funds for the Central Universities (No. 2018YJS008)
the National Natural Science Foundation of China (61471031, 61661021, 61531009)
Beijing Natural Science Foundation (L182018)
the Open Research Fund of National Mobile Communications Research Laboratory, Southeast University (No. 2017D14)
the State Key Laboratory of Rail Traffi c Control and Safety (Contract No. RCS2017K009)
Science and Technology Program of Jiangxi Province (20172BCB22016, 20171BBE50057)
Shenzhen Science and Technology Program under Grant (No. JCYJ20170817110410346)
