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Research Article | Open Access

Computation Offloading and Resource Allocation for E2E Tasks in Satellite Edge Computing Networks

Yunbo Qu1, Tingting Zhang1( ), Yuan Feng2, Tongyang Xu3, Zijian Guo1
Beijing Institute of Technology, Beijing 100854, China
China Research and Development Academy of Machinery Equipment, Beijing 100089, China
Newcastle University, Newcastle upon Tyne NE1 7RU, UK
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Abstract

Satellite edge computing shows promise in delivering seamless, low-latency services in challenging contexts such as deserts, oceans, and during natural disasters. Previous studies focused on the selection of suitable offloading nodes for task processing and result transmission to the original node. However, for end-to-end tasks, exemplified by applications like satellite-based remote sensing and forestry monitoring, the disparity between source and destination nodes emphasizes the importance of routing selection in data transmission. This aspect substantially limits the effectiveness of traditional computation offloading methods. In this paper, we formulate a joint computation offloading, routing, and multiple resource allocation optimization problem for end-to-end tasks in satellite edge computing networks. Moreover, to reduce the complexity of this nondeterministic polynomial-time hard (NP-hard) problem, we decompose it into the multidimensional resource allocation in fixed offloading and routing condition and the computation offloading and routing selection problem. An algorithm based on binary particle swarm optimization for joint computation offloading, routing, and multiple resource allocation optimization problem is proposed. Simulation results are presented to demonstrate the performance of our scheme compared with other baseline schemes.

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Space: Science & Technology
Article number: 0144

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Cite this article:
Qu Y, Zhang T, Feng Y, et al. Computation Offloading and Resource Allocation for E2E Tasks in Satellite Edge Computing Networks. Space: Science & Technology, 2024, 4: 0144. https://doi.org/10.34133/space.0144

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Received: 31 October 2023
Accepted: 02 March 2024
Published: 09 April 2024
© 2024 Yunbo Qu et al. Exclusive licensee Beijing Institute of Technology Press. No claim to original U.S. Government Works.

Distributed under a Creative Commons Attribution License 4.0 (CC BY 4.0).