Abstract

In the mobile edge computing (MEC) platform, tasks that are being performed often change due to mobile device migration. In order to improve the energy utilization of the MEC platform and the migration process of the mobile terminal and to ensure effective and continuous operation of services, dynamic service migration strategy with energy optimization is required. Aiming at the problem of energy consumption optimization of dynamic service migration with the far-near effect in mobile networks, this article proposes a dynamic service migration strategy with energy optimization, which ensures the performance requirements of the service by considering the minimum energy cost of the relevant equipment during the dynamic migration process. First, by analyzing the relationship between migration distance and equipment transmit power, the energy consumption model associated with the migration distance is established. Then, according to the task dynamic service migration scenario, the dynamic service migration energy consumption model is constructed, so as to obtain the reward function for migrating energy consumption. Finally, the dynamic service migration strategy with energy optimization is realized through the optimal migration energy consumption expectation, which is obtained by the optimal stopping theory. The experimental results show that the optimization strategy proposed in this article can effectively reduce the energy consumption of dynamic service migration in different simulation environments and can improve the dynamic migration performance.

Highlights

  • With the rapid development of mobile cloud computing technology, mobile cloud computing-based mobile application scenarios, such as mobile video calling, smart home management, and mobile terminal access to remote desktops, are becoming more diverse [1]

  • Based on the scenario of the Mobile edge computing (MEC) platform and mobile terminal comigration service, we propose a dynamic service migration strategy with energy optimization. e general idea of the strategy is as follows: Under the premise of given number of MEC platforms, the process of selecting the migration of the MEC platform by the mobile terminal is transformed into the optimal stopping problem, and the optimal dynamic service migration energy consumption threshold is obtained by using the optimal stopping theory

  • In order to test the performance of the strategy during the dynamic migration process, the MATLAB simulation tool is used to simulate the proposed migration strategy and compare it with other transmission strategies. is section compares each strategy from four aspects: average migration energy consumption, average effective data migration energy efficiency [23], average channel transmission power, and average migration distance energy efficiency

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Summary

Introduction

With the rapid development of mobile cloud computing technology, mobile cloud computing-based mobile application scenarios, such as mobile video calling, smart home management, and mobile terminal access to remote desktops, are becoming more diverse [1]. Based on the scenario of the MEC platform and mobile terminal comigration service, we propose a dynamic service migration strategy with energy optimization. E general idea of the strategy is as follows: Under the premise of given number of MEC platforms, the process of selecting the migration of the MEC platform by the mobile terminal is transformed into the optimal stopping problem, and the optimal dynamic service migration energy consumption threshold is obtained by using the optimal stopping theory. E proposed strategy is different from these existing optimization strategies: it is based on the combination of dynamic service migration research and the far-near problem in wireless network, and the MEC platform is selected according to the optimal migration energy consumption threshold to realize the dynamic service migration optimization strategy.

Related Research Work
Problem Description and Model Building
Model Establishment
Dynamic Service Migration Strategy with Energy Optimization
Conclusions
Full Text
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