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Task offloading and parameters optimization of MAR in multi-access edge computing
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AI
DOI：10.1016/j.eswa.2022.119379.png)
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Abstract
En 中文
With the development of mobile augmented reality (MAR) technology, the demand for MAR applications is increasing. However, MAR is rarely used in mobile devices due to its high computational and energy consumption. In this paper, we study the task offloading and parameters optimization of MAR applied to mobile devices in mobile edge computing. Considering the influence of the MAR client energy consumption, service delay and detection accuracy in the task offloading and parameters optimization process, we design a function to evaluate MAR client energy efficiency. The problem of task offloading and parameters optimization is formulated to minimize energy efficiency function under the limitation of MAR task completion time and wireless bandwidth resources. To solve this problem, we propose a server selection and parameters optimization (SSPO) algorithm to realize client task offloading and parameters optimization. The SSPO algorithm first generates priority queue of tasks. Based on the order of priority queue, tasks are offloaded to appropriate mobile edge server according to the analytic hierarchy process. After that, the parameters are calculated and the tasks are redistributed according to the completion time until the energy efficiency function converges. Simulation results show that the proposed algorithm is better than the comparison algorithm.
Keywords:
Energy efficiency
MAR
Mobile edge server
Parameters optimization
Task offloading
Journal
IF:
7.5
Papers:
2.9W
Citations:
10.2W