Research Article
Cache-Enhanced Task Offloading for eIoT with Mobile Edge Computing
@INPROCEEDINGS{10.1007/978-3-030-64002-6_5, author={Zhifeng Li and Jie Bai and Haonan Zhang and Wei Bai and Yongmin Cao and Liwen Wu and Jianying Dong and Yanshan Deng}, title={Cache-Enhanced Task Offloading for eIoT with Mobile Edge Computing}, proceedings={Mobile Networks and Management. 10th EAI International Conference, MONAMI 2020, Chiba, Japan, November 10--12, 2020, Proceedings}, proceedings_a={MONAMI}, year={2020}, month={12}, keywords={eIoT Collaborative caching MEC Computing offloading}, doi={10.1007/978-3-030-64002-6_5} }- Zhifeng Li
Jie Bai
Haonan Zhang
Wei Bai
Yongmin Cao
Liwen Wu
Jianying Dong
Yanshan Deng
Year: 2020
Cache-Enhanced Task Offloading for eIoT with Mobile Edge Computing
MONAMI
Springer
DOI: 10.1007/978-3-030-64002-6_5
Abstract
With the continuous development and improvement of 5G networks, many emerging technology architectures have been introduced to support 5G service requirements. As one of them, mobile edge computing can meet the exponentially increasing computing requirements, and with its advantages of being more efficient, smarter, and more flexible, it can be well adapted to smart grid scenarios. However, most of the existing research contents of the eIoT focus on the research of computing offloading and content caching separately, ignoring the problem of reusability of some computing results. This paper considers the certain content caching capabilities of the MEC system itself, and aims to design a cache-enhanced MEC eIoT. The model includes offloading, calculating and backhaul for uncached task and downloading of cached content. On the other hand, the problem of task diversity and inspection robot mobility is fully analyzed. Subsequently, we studied the impact of caching capabilities on computing power to get the best MEC server parameter information. Based on the above research, this paper proposes a cache enhanced offload strategy and a collaborative scheduling algorithm to optimize the total delay of all tasks of the inspection robot in the eIoT. Simulation results show that the strategy can effectively reduce the computational offloading latency.
