Abstract
The rapid development of smart cities has led to a large number of IoT devices connected to the network. The introduction of mobile edge computing technology can solve the problem of increasing network congestion caused by centralized cloud computing and large-scale data transmission. However, the diverse demands of IoT tasks and the mobility of users still pose challenges to network transmission and task processing. It is easy to cause unbalanced load of edge servers, resulting in high network energy consumption. To solve the above problems, a cloud edge terminal collaboration network task immigration model was established and a task immigration mechanism for Internet of things was proposed. In the proposed mechanism, deep reinforcement learning algorithm is used to solve immigration policies, and user mobility is considered to meet the resource demand of the task in the region. The simulation results show that the proposed mechanism can reduce the service request delay, system energy consumption and enhance user experience.
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Acknowledgment
This work is supported by the Science and Technology Project of State Grid Corporation of China: Research and Application of Key Technologies in Virtual Operation of Information and Communication Resources. The corresponding author is Yifei Xing with e-mail address xingyifei@bupt.edu.cn.
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Xing, Y., Yang, C., Zhang, H., Xu, S., Shao, S., Wang, S. (2022). A Computation Task Immigration Mechanism for Internet of Things Based on Deep Reinforcement Learning. In: Liu, Q., Liu, X., Chen, B., Zhang, Y., Peng, J. (eds) Proceedings of the 11th International Conference on Computer Engineering and Networks. Lecture Notes in Electrical Engineering, vol 808. Springer, Singapore. https://doi.org/10.1007/978-981-16-6554-7_44
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DOI: https://doi.org/10.1007/978-981-16-6554-7_44
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