期刊
IEEE TRANSACTIONS ON NETWORK SCIENCE AND ENGINEERING
卷 7, 期 4, 页码 2598-2607出版社
IEEE COMPUTER SOC
DOI: 10.1109/TNSE.2020.2988052
关键词
Cloud computing; Task analysis; Energy consumption; Delays; Heuristic algorithms; Big Data; Edge computing; Intelligent dynamic offloading; big data; edge computing; smart IoT systems
资金
- Open Fund of the Key Laboratory of Data mining and Intelligent Recommendation, Fujian Province University [DM201902]
- General Projects of Social Sciences in Fujian Province [FJ2018B038]
- National Natural Science Foundation of China [61872154, 61772148, 61672441]
- Natural Science Foundation of Fujian Province of China [2018J01092]
- Fujian Provincial Outstanding Youth Scientific Research Personnel Training Program
Intelligent networking and big data analytics are two important pillars for the operation of systems. Edge computing is frequently used in smart IoT systems, particularly in those which cannot be served efficiently through cloud computing due to the limitations in bandwidth, latency and Internet connectivity. However, applications always generate a large amount of data, which are pre-programmed and predefined to run on the cloud or edge platform and can't be changed at run time. The applications may gain better performance if they synergistically run on the cloud and edge platform. In this study, a novel algorithm called Dynamic Switching Algorithm is proposed to ensure intelligent dynamics where all tasks are either offloaded on cloud or edge according to the system's real-time conditions. We further divide applications into four types based on their real-time requirements. Each type of application is set to a reasonable latency to make sure the system to have less processing time. The results demonstrate that our method outperforms two state-of-the-art methods, decreasing both the average delay and energy consumption of offloading by 8.17%similar to 66.90% and 3.76%similar to 78.60% respectively. The experimental evaluations show that the performance of the proposed method could effectively offload tasks in smart IoT systems.
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