Task Offloading and Resource Allocation Strategy in Non-Terrestrial Networks for Continuous Distributed Task Scenarios
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View abstract on PubMed
Summary
This summary is machine-generated.This study introduces a novel cloud-edge architecture for non-terrestrial networks, optimizing task offloading for 6G and IoT. A new deep reinforcement learning algorithm significantly reduces system costs by efficiently managing resources.
Area Of Science
- Computer Science
- Electrical Engineering
- Telecommunications
Background
- Non-terrestrial networks are vital for 6G, IoT, and digitalization.
- Existing task classification methods struggle with continuously distributed task attributes.
- A gap exists in handling quantitative continuous task requirements in edge computing.
Purpose Of The Study
- To model a multi-task scenario with continuously distributed attributes for edge computing.
- To propose a three-tier cloud-edge collaborative offloading architecture.
- To minimize system costs by integrating UAV load balancing and satellite energy efficiency.
Main Methods
- Developed a three-tier architecture: UAV edge nodes, LEO satellites, and ground cloud data centers.
- Formulated a system cost minimization problem.
- Proposed a two-layer multi-type-agent deep reinforcement learning (TMDRL) algorithm, integrating DQN and DDPG.
Main Results
- The TMDRL algorithm effectively optimizes task offloading and resource allocation.
- The proposed architecture and algorithm address quantitatively continuous task requirements.
- Simulation results show a 7.82% reduction in system cost compared to baseline methods.
Conclusions
- The developed TMDRL algorithm provides an efficient solution for task offloading in non-terrestrial edge computing.
- The three-tier architecture enhances system performance and cost-efficiency.
- This work contributes to the advancement of 6G and ubiquitous digital services.
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