卫星
云计算
近地轨道
计算机科学
任务(项目管理)
GSM演进的增强数据速率
资源(消歧)
卫星广播
地球观测卫星
地心轨道
遥感
资源管理(计算)
轨道(动力学)
天体生物学
分布式计算
电信
航空航天工程
地质学
计算机网络
系统工程
物理
工程类
操作系统
标识
DOI:10.1109/icct59356.2023.10419596
摘要
With the rapid advancement of satellite communication technology, integrating decentralized satellite computing capabilities through low earth orbit (LEO) satellite edge computing has emerged as a promising solution. However, computation offloading and resource allocation in the mobile edge computing environment face significant challenges due to dynamic system states and evolving user demands. This paper presents a cloud-edge collaborative computation offloading model that determines optimal offloading strategies and resource allocation based on task features. Initially, a network model is established with well-defined task and resource parameters, which includes ground terminals, satellites, and clouds. Then, a joint latency and energy consumption optimization problem is formulated and described using the Markov decision process (MDP). Finally, we propose a soft actor-critic-based optimal task offloading and resource allocation (SAC-OTORA) algorithm to minimize system la-tency and energy consumption simultaneously. Simulation results demonstrate that the proposed scheme outperforms the existing schemes, achieving lower latency and energy consumption.
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