Algorithm of task offloading and resource allocation based on reinforcement learning in edge computing

Zhao, Jianan; Hu, Xiaohui; Du, Xinxin

doi:10.1109/itnec52019.2021.9587049

Cited by 5 publications

(2 citation statements)

References 8 publications

Supporting

Mentioning

Contrasting

Unclassified

Order By: Relevance

“…现有的深度计算任务自适应分割方法通过优化分布式深度学习推断中的输入数据复用、中间数据传输和内存开销, 在每个设备上进行单独优化, 并在跨设备上进行全局验证. 研究主要分为两类: 基于深度神经网络层分割的串行协同计算 [36,37] 和基于层内细粒度模块分割的并行协同计算 [38∼40]…”

Section: 逐层的分布式深度计算任务分割与卸载方法指在进行深度学习模型推断时将模型的各个层次unclassified

Lightweight sensing-computing-decision collaboration enhancement for multi-mobile terminals

GAO,

LIU,

GUO

et al. 2024

Sci. Sin.-Inf.

View full text Add to dashboard Cite

Section: 逐层的分布式深度计算任务分割与卸载方法指在进行深度学习模型推断时将模型的各个层次unclassified

Lightweight sensing-computing-decision collaboration enhancement for multi-mobile terminals

GAO,

LIU,

GUO

et al. 2024

Sci. Sin.-Inf.

View full text Add to dashboard Cite

“…In the IoT device-UAV channel, the data offloading delay for the IoT device task S i to offload the task to the UAV includes both transmission delay, propagation delay, transmission delay T tran im and propagation delay T prop im , which is given by the following equation [35].…”

Section: Task Offloading and Computingmentioning

confidence: 99%

Resource Allocation and Offloading Strategy for UAV-Assisted LEO Satellite Edge Computing

Zhang

Jiang

et al. 2023

Drones

View full text Add to dashboard Cite

In emergency situations, such as earthquakes, landslides and other natural disasters, the terrestrial communications infrastructure is severely disrupted and unable to provide services to terrestrial IoT devices. However, tasks in emergency scenarios often require high levels of computing power and energy supply that cannot be processed quickly enough by devices locally and require computational offloading. In addition, offloading tasks to server-equipped edge base stations may not always be feasible due to the lack of infrastructure or distance. Since Low Orbit Satellites (LEO) have abundant computing resources, and Unmanned Aerial Vehicles (UAVs) have flexible deployment, offloading tasks to LEO satellite edge servers via UAVs becomes straightforward, which provides computing services to ground-based devices. Therefore, this paper investigates the computational tasks and resource allocation in a UAV-assisted multi-layer LEO satellite network, taking into account satellite computing resources and device task volumes. In order to minimise the weighted sum of energy consumption and delay in the system, the problem is formulated as a constrained optimisation problem, which is then transformed into a Markov Decision Problem (MDP). We propose a UAV-assisted airspace integration network architecture, and a Deep Deterministic Policy Gradient and Long short-term memory (DDPG-LSTM)-based task offloading and resource allocation algorithm to solve the problem. Simulation results demonstrate that the solution outperforms the baseline approach and that our framework and algorithm have the potential to provide reliable communication services in emergency situations.

show abstract

Resource Allocation in Edge Computing Environment Using Deterministic Policy Gradient Algorithm

Shabariram,

Shanthi,

Ponnuswamy

et al. 2024

2024 10th International Conference on Advanced Computing and Communication Systems (ICACCS)

View full text Add to dashboard Cite

Algorithm of task offloading and resource allocation based on reinforcement learning in edge computing

Cited by 5 publications

References 8 publications

Lightweight sensing-computing-decision collaboration enhancement for multi-mobile terminals

Lightweight sensing-computing-decision collaboration enhancement for multi-mobile terminals

Resource Allocation and Offloading Strategy for UAV-Assisted LEO Satellite Edge Computing

Resource Allocation in Edge Computing Environment Using Deterministic Policy Gradient Algorithm

Contact Info

Product

Resources

About