Collaborative Computation Offloading for Blocked Jobs in LEO Satellite-Ground Integrated Networks
Peng, Pei Xu, Tianheng Zou, Yulong Xu, Wei Rui, Yun Guizani, Mohsen
Peng, Pei
Xu, Tianheng
Zou, Yulong
Xu, Wei
Rui, Yun
Guizani, Mohsen
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Department
Machine Learning
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Type
Journal article
Date
2025
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Language
English
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Abstract
This letter investigates blocked job offloading in a satellite-ground integrated network, which allows each low-earth orbit (LEO) satellite to execute more jobs on-board instead of sending the raw data to ground users. We propose an approximation offloading model to approximate the satellite-ground integrated network and analyze the average execution time, which is defined as when the user receives the raw data or processed result from the LEO satellite. Furthermore, we propose the maximum equivalent arrival rate and blocked job offloading algorithms. Numerical results validate the approximation model’s effectiveness and the proposed algorithms’ performance advantages and indicate a way to select the proper algorithm.
Citation
P. Peng, T. Xu, Y. Zou, W. Xu, Y. Rui and M. Guizani, "Collaborative Computation Offloading for Blocked Jobs in LEO Satellite-Ground Integrated Networks," in IEEE Internet of Things Journal, doi: 10.1109/JIOT.2025.3632149.
Source
IEEE Internet of Things Journal
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Keywords
LEO satellite-ground integrated network, Collaborative computing, Blocking system, Job offloading
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Publisher
IEEE