An energy saving based on task migration for mobile edge computing

Yichuan Wang,He Zhu,Yue Kong,Lei Zhu,Xinhong Hei,Wenjiang Ji

doi:10.1186/s13638-019-1469-2

Yichuan Wang, He Zhu + Show 4 more

Open Access

https://doi.org/10.1186/s13638-019-1469-2

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Abstract

Mobile edge computing (MEC), as the key technology to improve user experience in a 5G network, can effectively reduce network transmission delay. Task migration can migrate complex tasks to remote edge servers through wireless networks, solving the problems of insufficient computing capacity and limited battery capacity of mobile terminals. Therefore, in order to solve the problem of “how to realize low-energy migration of complex dependent applications,” a subtask partitioning model with minimum energy consumption is constructed based on the relationship between the subtasks. Aiming at the problem of execution time constraints, the genetic algorithm is used to find the optimal solution, and the migration decision results of each subtask are obtained. In addition, an improved algorithm based on a genetic algorithm is proposed to dynamically adjust the optimal solution obtained by genetic algorithm by determining the proportion of task energy consumption and mobile phone residual power. According to the experimental results, it can be concluded that the fine-grained task migration strategy combines the advantages of mobile edge computing, not only satisfies the smooth execution of tasks, but also reduces the energy consumption of terminal mobile devices. In addition, experiments show that the improved algorithm is more in line with users’ expectations. When the residual power of mobile devices is reduced to a certain value, tasks are migrated to MEC server to prolong standby time.

Highlights

With the Internet of Things and the mobile Internet are booming, people have entered the era of the Internet of Everything
With the rapid increase in the number of network edge devices, mass data is generated by the perception layer of the Internet of Things, which leads to a sharp increase in the load of the cloud computing network, resulting in a long network delay [1]
In the analysis of experimental results, we select several common migration algorithms compare with fine-grained migration based on generation algorithm (FGMBGA) which is the algorithm this paper proposed on their performance

Summary

Introduction

With the Internet of Things and the mobile Internet are booming, people have entered the era of the Internet of Everything. The task of intelligent terminal devices can be migrated to the mobile edge computing server to solve the problem of insufficient mobile terminal resources, and effectively reduce the delay and energy consumption. These features make it a key technology to improve the 5G network [3] user experience in the future. The existing task migration strategy is to make the migration decision under the premise that the migration service node has been established [4] It does not take into account the scenarios when the multi-service nodes are available, and cannot give full play to the characteristics and advantages of mobile edge computing.

Background

Energy-saving strategy

Fine-grained linear chain task decomposition

Fine-grained directed acyclic graph task decomposition

Findings

Conclusion