Multi-task offloading scheme for UAV-enabled fog computing networks

Xujie Li,Buyankhishig Ulziinyam,Ying Sun,Lingjie Zhou

doi:10.1186/s13638-020-01825-y

Abstract

In unmanned aerial vehicle (UAV)-enabled fog computing networks, how to efficiently offload multiple tasks to the computing nodes is a challenging combinatorial optimization problem. In this paper, in order to optimize the total delay for the UAV-enabled fog computing networks, a simple scheduling algorithm and a multi-task offloading scheme based on fireworks algorithm (FWA) are proposed. First, the system model of multiple tasks offloading in UAV-enabled fog computing networks is described in detail. Then, a simple scheduling algorithm is proposed to optimize the delay of the tasks allocated to a single node. Based on the scheduling algorithm, a multi-task offloading scheme for all tasks and all computing nodes is presented. Finally, simulation results show that the performance of a proposed scheduling algorithm and offloading strategy outperforms than that of a genetic algorithm and a random algorithm. This result can provide an effective optimization for multi-task offloading in UAV-enabled fog computing networks.

Highlights

1.1 MotivationIn recent years, with the popularization of smartphones and various new applications, wireless data traffic has increased thousands of times [1, 2]
An efficient schedule algorithm for the tasks to be allocated one computing node and a multi-task offloading scheme based on improving fireworks algorithm for unmanned aerial vehicle (UAV)-enabled fog computing networks are proposed
We assume that N Computing node (CN) are uniformly distributed in the cellular with the radius of R

Summary

Motivation

With the popularization of smartphones and various new applications, wireless data traffic has increased thousands of times [1, 2]. In paper [12], the authors study the task offloading problem between the Internet of Things mobile devices (IMDs) and the UAV to minimize the overall energy consumption for UAV-aided edge computing networks. In paper [14], the authors solve the problem of offloading heavy computation tasks of UAVs while achieving the best possible tradeoff between energy consumption, time delay, and computation cost. In paper [16], the authors propose a novel game-theoretic and reinforcement learning framework for computational offloading for the mobile edge computing network which is operated by multiple different service providers. An efficient schedule algorithm for the tasks to be allocated one computing node and a multi-task offloading scheme based on improving fireworks algorithm for UAV-enabled fog computing networks are proposed.

Computing model

TASK j is allocated to CNi 0 TASK j is not allocated to CNi ð6Þ

Results and discussion

Conclusions