Distributed Computation Offloading in Mobile Fog Computing: A Deep Neural Network Approach

Yang, Zhongjun; Bai, Wenle

doi:10.1109/lcomm.2021.3138800

Cited by 4 publications

(7 citation statements)

References 11 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…An increasing number of researches on distributed offloading techniques has been conducted to address these difficulties. In [20][21][22][23][24], hybrid computation offloading architectures that adopt centralized training and distributed execution are proposed. A distributed DNN offloading algorithm (DDOA) is proposed in [20], multiple distributed DNNs working in parallel are used to create offloading decisions, these DNNs are enhanced further by using the newly generated decisions as a public training set and the back-propagation method with cross-entropy as the loss function.…”

Section: Related Workmentioning

confidence: 99%

“…In [20][21][22][23][24], hybrid computation offloading architectures that adopt centralized training and distributed execution are proposed. A distributed DNN offloading algorithm (DDOA) is proposed in [20], multiple distributed DNNs working in parallel are used to create offloading decisions, these DNNs are enhanced further by using the newly generated decisions as a public training set and the back-propagation method with cross-entropy as the loss function. A Multi-Agent DRL algorithm is proposed in [21] to maximize energy consumption while simultaneously optimizing power control, resource allocation, and user equipment association, considering the cost of offloading and MEC server pricing.…”

Section: Related Workmentioning

confidence: 99%

“…• DDOA [20]: A distributed DNN offloading algorithm, uses two OGs, each one simultaneously making an offloading decision for 3 MDs. .…”

Section: Comparison Analysismentioning

confidence: 99%

See 2 more Smart Citations

DRL-Based Distributed Task Offloading Framework in Edge-Cloud Environment

Nashaat,

Hashem,

Rizk

et al. 2024

IEEE Access

View full text Add to dashboard Cite

The Internet of Things (IoT), real-time media streaming, and other related technologies have increased due to the rapid development of wireless communication technologies and the enormous growth of computation, storage, and data transmission tasks. Edge-Cloud Computing (ECC) is a technology that can be used to better meet the diverse needs of IoT users, it combines the benefits of Mobile Cloud Computing (MCC) and Mobile Edge Computing (MEC) to meet energy consumption and delay requirements, and achieve more stable and affordable task execution. The most significant challenge in ECC is making realtime task offloading decisions. In order to generate offloading decisions in ECC environments in an efficient and near optimal manner, a Deep Reinforcement Learning (DRL)-based Distributed task Offloading (DRL-DO) framework is proposed. Simulation results demonstrate the accuracy of the DRL-DO framework, it achieves high Gain Ratio (GR) and greatly reduces the energy consumption, response time, while attaining moderate time cost compared with other offloading algorithms.

show abstract

Section: Related Workmentioning

confidence: 99%

Section: Related Workmentioning

confidence: 99%

See 1 more Smart Citation

DRL-Based Distributed Task Offloading Framework in Edge-Cloud Environment

Nashaat,

Hashem,

Rizk

et al. 2024

IEEE Access

View full text Add to dashboard Cite

show abstract

“…In order to solve the problem of limited resource in the servers in the edge computing system, Tang et al [30] jointly optimize the problem of DNN partition and resource allocation, and obtain ideal efficiency through iterator alternating optimization algorithm. In order to solve the problem of offloading decisions and bandwidth allocation in the edge computing system, Yang et al [31] propose a distributed DNN offloading algorithm (DDOA) algorithm to generate offloading decisions through multiple parallel DNNs and jointly optimize the weighted sum of delay and energy consumption. To solve the bandwidth allocation problem, the DDOA algorithm uses the orthogonal frequency division multiple access (OFDMA) technique to divide the bandwidth, and the simulation results show the superiority of the performance.…”

Section: Related Workmentioning

confidence: 99%

“…By setting parameter ϕ, we can more intuitively express the relationship between delay and energy consumption. The optimization objective of DDOA algorithm [31] is Delay-Energy Weighted Sum (DEWS) metric:…”

Section: Problem Formulationmentioning

confidence: 99%

DNNs Based Computation Offloading for LEO Satellite Edge Computing

Jia

Zhang

et al. 2022

Electronics

View full text Add to dashboard Cite

Huge low earth orbit (LEO) satellite networks can achieve global coverage with low latency. In addition, mobile edge computing (MEC) servers can be mounted on LEO satellites to provide computing offloading services for users in remote areas. A multi−user multi−task system model is modeled and the problem of user’s offloading decisions and bandwidth allocation is formulated as a mixed integer programming problem to minimize the system utility function expressed as the weighted sum of the system energy consumption and delay. However, it cannot be effectively solved by general optimizations. Thus, a deep learning−based offloading algorithm for LEO satellite edge computing networks is proposed to generate offloading decisions through multiple parallel deep neural networks (DNNs) and store the newly generated optimal offloading decisions in memory to improve all DNNs to obtain near−optimal offloading decisions. Moreover, the optimal bandwidth allocation scheme of the system is theoretically derived for the user’s bandwidth allocation problem. The simulation results show that the proposed algorithm can achieve a good convergence effect within a small number of training steps, and obtain the optimal system utility function values compared with the comparative algorithms under different system parameters, and the time cost of the system and DNNs is very satisfactory.

show abstract