Journal
FUTURE GENERATION COMPUTER SYSTEMS-THE INTERNATIONAL JOURNAL OF ESCIENCE
Volume 95, Issue -, Pages 522-533Publisher
ELSEVIER
DOI: 10.1016/j.future.2018.12.055
Keywords
IoT; Big data; Cloud-edge computing; Computation offloading; Energy consumption
Categories
Funding
- National Science Foundation of China [61702277, 61872219, 6177228, 616722763]
- Startup Foundation for Introducing Talent of NUIST
- State Key Laboratory for Novel Software Technology, Nanjing University, China [KFKT2017B04]
- Priority Academic Program Development of Jiangsu Higher Education Institutions (PAPD), China fund
- Jiangsu Collaborative Innovation Center on Atmospheric Environment and Equipment Technology (CICAEET), China
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The Internet of mobile things is a burgeoning technique that generates, stores and processes big real-time data to render rich services for mobile users. In order to mitigate conflicts between the resource limitation of mobile devices and users' demands of decreasing processing latency as well as prolonging battery life, it spurs a popular wave of offloading mobile applications for execution to centralized and decentralized data centers, such as cloud and edge servers. Due to the complexity and difference of mobile big data, arbitrarily offloading the mobile applications poses a remarkable challenge to optimizing the execution time and the energy consumption for mobile devices, despite the improved performance of Internet of Things (IoT) in cloud-edge computing. To address this challenge, we propose a computation offloading method, named COM, for IoT-enabled cloud-edge computing. Specifically, a system model is investigated, including the execution time and energy consumption for mobile devices. Then dynamic schedules of data/control-constrained computing tasks are confirmed. In addition, NSGA-III (non-dominated sorting genetic algorithm III) is employed to address the multi-objective optimization problem of task offloading in cloud-edge computing. Finally, systematic experiments and comprehensive simulations are conducted to corroborate the efficiency of our proposed method. (C) 2019 Elsevier B.V. All rights reserved.
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