Open Access

ARTICLE

Satellite-Air-Terrestrial Cloud Edge Collaborative Networks: Architecture, Multi-Node Task Processing and Computation

by Sai Liu¹, Zhenjiang Zhang^1,*, Guangjie Han², Bo Shen¹

1 School of Electronic Information Engineering, Beijing Jiaotong University, Beijing, 100044, China
2 School of Internet of Things Engineering, Hohai University, Nanjing, 211100, China

* Corresponding Author: Zhenjiang Zhang. Email:

(This article belongs to the Special Issue: Optimization Problems Based on Mathematical Algorithms and Soft Computing)

Intelligent Automation & Soft Computing 2023, 37(3), 2651-2668. https://doi.org/10.32604/iasc.2023.038477

Received 14 December 2022; Accepted 12 April 2023; Issue published 11 September 2023

Abstract

Integrated satellite-terrestrial network (ISTN) has been considered a novel network architecture to achieve global three-dimensional coverage and ultra-wide area broadband access anytime and anywhere. Being a promising paradigm, cloud computing and mobile edge computing (MEC) have been identified as key technology enablers for ISTN to further improve quality of service and business continuity. However, most of the existing ISTN studies based on cloud computing and MEC regard satellite networks as relay networks, ignoring the feasibility of directly deploying cloud computing nodes and edge computing nodes on satellites. In addition, most computing tasks are transferred to cloud servers or offloaded to nearby edge servers, the layered design of integrated satellite-air-terrestrial architecture and the cloud-edge-device cooperative processing problems have not been fully considered. Therefore, different from previous works, this paper proposed a novel satellite-air-terrestrial layered architecture for cloud-edge-device collaboration, named SATCECN. Then this paper analyzes the appropriate deployment locations of cloud servers and edge servers in ISTN, and describes the processing flow of typical satellite computing tasks. For computing resource allocation problems, this paper proposed a device-edge-cloud Multi-node Cross-layer Collaboration Computing (MCCC) method to find the optimal task allocation strategy that minimizes the task completion delay and the weighted system energy consumption. Furthermore, the approximate optimal solutions of the optimization model are obtained by using successive convex approximation algorithm, and the outstanding advantages of the proposed method in reducing system energy consumption and task execution delay are verified through experiments. Finally, some potential issues and directions for future research are highlighted.

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Keywords

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