Computing Offloading Strategy Using Improved Genetic Algorithm in Mobile Edge Computing System

Aiming at the problem that traditional fixed base stations cannot provide good signal coverage due to geographical factors, which may reduce the efficiency of task offloading, a collaborate task offloading strategy using improved genetic algorithm in mobile edge computing (MEC) is proposed by introducing the unmanned aerial vehicle (UAV) cluster. First, for the scenario of the UAV cluster serving multiple ground terminals, a collaborative task offloading model is formulated to offload the tasks to UAVs or the base station selectively. Then, an objective function and related constraints are put forward to minimize the time delay and energy consumption by analysis of those in the communication and computing process in the system while considering many factors. Then, the improved genetic algorithm is introduced to solve the optimization problem, obtaining the optimal collaborative task offloading strategy. To verify the performance of the proposed method, simulations are conducted on MATLAB. Simulation results showed that the joint utilization of UAV and MEC improves the offloading efficiency of the proposed strategy. When the number of UAVs is 12, the total utility is up to 1.83 and the task completion time does not exceed 110 ms. In this case, the task can be reasonably offloaded to UAVs or accomplished locally.

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Blockchain-based mobile edge computing system

Information Sciences ◽

10.1016/j.ins.2021.01.050 ◽

2021 ◽

Vol 561 ◽

pp. 70-80

Author(s):

Guangshun Li ◽

Xinrong Ren ◽

Junhua Wu ◽

Wanting Ji ◽

Haili Yu ◽

...

Keyword(s):

Computing System ◽

Edge Computing ◽

Mobile Edge Computing

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Adaptive offloading in mobile-edge computing for ultra-dense cellular networks based on genetic algorithm

Journal of Cloud Computing Advances Systems and Applications ◽

10.1186/s13677-021-00232-y ◽

2021 ◽

Vol 10 (1) ◽

Author(s):

Zhuofan Liao ◽

Jingsheng Peng ◽

Bing Xiong ◽

Jiawei Huang

Keyword(s):

Genetic Algorithm ◽

Energy Consumption ◽

Cellular Networks ◽

Physical Distance ◽

Edge Computing ◽

Base Stations ◽

Mobile Edge Computing ◽

Total Energy Consumption ◽

Average Delay ◽

Server Selection

AbstractWith the combination of Mobile Edge Computing (MEC) and the next generation cellular networks, computation requests from end devices can be offloaded promptly and accurately by edge servers equipped on Base Stations (BSs). However, due to the densified heterogeneous deployment of BSs, the end device may be covered by more than one BS, which brings new challenges for offloading decision, that is whether and where to offload computing tasks for low latency and energy cost. This paper formulates a multi-user-to-multi-servers (MUMS) edge computing problem in ultra-dense cellular networks. The MUMS problem is divided and conquered by two phases, which are server selection and offloading decision. For the server selection phases, mobile users are grouped to one BS considering both physical distance and workload. After the grouping, the original problem is divided into parallel multi-user-to-one-server offloading decision subproblems. To get fast and near-optimal solutions for these subproblems, a distributed offloading strategy based on a binary-coded genetic algorithm is designed to get an adaptive offloading decision. Convergence analysis of the genetic algorithm is given and extensive simulations show that the proposed strategy significantly reduces the average latency and energy consumption of mobile devices. Compared with the state-of-the-art offloading researches, our strategy reduces the average delay by 56% and total energy consumption by 14% in the ultra-dense cellular networks.

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