Energy-efficient computation offloading and resource allocation in fog computing for Internet of Everything

• Published in: China communications Vol. 16; no. 3; pp. 32 - 41
• Main Authors: Li, Qiuping, Zhao, Junhui, Gong, Yi, Zhang, Qingmiao
• Format: Journal Article
• Language: English
• Published: China Institute of Communications 01-03-2019; School of Electronic and Information Engineering, Beijing Jiaotong University, Beijing 100044, China; Shenzhen Engineering Laboratory of Intelligent Information Processing for IoT, Southern University of Science and Technology, Shenzhen 518055, China%School of Electronic and Information Engineering, Beijing Jiaotong University, Beijing 100044, China; School of Information Engineering, East China Jiaotong University, Nanchang 330013, China%Shenzhen Engineering Laboratory of Intelligent Information Processing for IoT, Southern University of Science and Technology, Shenzhen 518055, China%School of Information Engineering, East China Jiaotong University, Nanchang 330013, China
• Subjects: Cloud computing; computation offloading; Computational modeling; Edge computing; Energy consumption; fog computing; Internet of Things; IoE; resource allocation; Resource management; Task analysis; fog computing; resource allocation; cloud computing; computation offloading; IoE
• ISSN: 1673-5447
• DOI: 10.12676/j.cc.2019.03.004

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.