Randomization-Based Dynamic Programming Offloading Algorithm for Mobile Fog Computing

Wenle Bai,Zhongjun Yang,Jianhong Zhang,Rajiv Kumar,Xin Liu

doi:10.1155/2021/4348511

Abstract

Offloading to fog servers makes it possible to process heavy computational load tasks in local devices. However, since the generation problem of offloading decisions is an N-P problem, it cannot be solved optimally or traditionally, especially in multitask offloading scenarios. Hence, this paper has proposed a randomization-based dynamic programming offloading algorithm, based on genetic optimization theory, to solve the offloading decision generation problem in mobile fog computing. The algorithm innovatively designs a dynamic programming table-filling approach, i.e., iteratively generates a set of randomized offloading decisions. If some in these sets improve the decisions in the DP table, then they will be merged into the table. The iterated DP table is also used to improve the set of decisions generated in the iteration to obtain the optimal offloading approximate solution. Extensive simulations show that the proposed DPOA can generate decisions within 3 ms and the benefit is especially significant when users are in multitask offloading scenarios.

Highlights

With the increasing popularity of smart devices, smart applications provide a rich user experience while placing stringent demands on computing power
Aiming at minimizing the energy consumption, the work in [5] develops an offloading interaction model based on the auction mechanism, while the authors of [6] propose an incentive propagation mechanism (IPM) algorithm
With the boon of deep learning in recent years, some studies have combined deep learning with the mobile fog computing (MFC). e study in [10] proposes a joint computational offloading and resource allocation algorithm based on deep reinforcement learning (DRL), which is used for deriving a suboptimal solution for the optimization problem

Summary

Introduction

With the increasing popularity of smart devices, smart applications provide a rich user experience while placing stringent demands on computing power. Due to the limitations of mobile devices, it becomes a challenge to maintain the quality of service when traditional devices are faced with heavy computational demands To solve this problem, mobile fog computing (MFC) [1] has been proposed to compensate for the lack of computing power in local devices. E study in [10] proposes a joint computational offloading and resource allocation algorithm based on deep reinforcement learning (DRL), which is used for deriving a suboptimal solution for the optimization problem. Attention is paid to the task offloading problem in an MFC network and a dynamic programming offloading algorithm (DPOA) based on randomization is proposed to solve the offloading decision generation problem.

Network Model and Problem Formulation

Evaluation and Simulation

Conclusion