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Electronics and Telecommunications Research Institute (한국전자통신연구원)
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Energy efficiency task scheduling for battery level-aware mobile edge computing in heterogeneous networks

  • Xie, Zhigang (College of Computer Science and Engineering, Northeastern University) ;
  • Song, Xin (College of Computer Science and Engineering, Northeastern University) ;
  • Cao, Jing (Engineering Optimization and Smart Antenna Research Institute, Northeastern University at Qinhuangdao) ;
  • Xu, Siyang (College of Computer Science and Engineering, Northeastern University)
  • Received : 2021.10.25
  • Accepted : 2022.03.22
  • Published : 2022.10.10
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Abstract

This paper focuses on a mobile edge-computing-enabled heterogeneous network. A battery level-aware task-scheduling framework is proposed to improve the energy efficiency and prolong the operating hours of battery-powered mobile devices. The formulated optimization problem is a typical mixed-integer nonlinear programming problem. To solve this nondeterministic polynomial (NP)-hard problem, a decomposition-based task-scheduling algorithm is proposed. Using an alternating optimization technology, the original problem is divided into three subproblems. In the outer loop, task offloading decisions are yielded using a pruning search algorithm for the task offloading subproblem. In the inner loop, closed-form solutions for computational resource allocation subproblems are derived using the Lagrangian multiplier method. Then, it is proven that the transmitted power-allocation subproblem is a unimodal problem; this subproblem is solved using a gradient-based bisection search algorithm. The simulation results demonstrate that the proposed framework achieves better energy efficiency than other frameworks. Additionally, the impact of the battery level-aware scheme on the operating hours of battery-powered mobile devices is also investigated.

Keywords

Acknowledgement

This research work was supported by the National Nature Science Foundation of China under Grants No. 61473066 and No. 61601109, the Fundamental Research Funds for Central Universities under Grant No. N152305001, and the Natural Science Foundation of Hebei Province under Grant No. F2021501020.

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