Load-Balanced Collaborative Offloading for LEO Satellite Networks

He, Peng; Hu, Jiaojiao; Fan, Xinyue; Wang, Dapeng; Wang, Ruyan; Cui, Yaping

doi:10.1109/jiot.2023.3280956

Cited by 6 publications

(1 citation statement)

References 33 publications

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“…Considering the characteristics of global seamless coverage and low transmission latency for LEO satellite networks, satellite edge caching, as viewed as a new paradigm, can provide edge caching services for terminal users, where caching servers are placed on LEO satellites. However, there are strictly limited on-board resources for satellite networks, which cannot meet with the service requirements of users when a large number of terminal users arrive [12,13]. By combining the advantages of terrestrial and satellite networks, cooperative content caching in integrated satelliteterrestrial networks has been a promising approach to provide content services for terminal users on-demand in a flexible and efficient way [14,15].…”

Section: Introductionmentioning

confidence: 99%

Cooperative Caching and Resource Allocation in Integrated Satellite–Terrestrial Networks

Gao,

Shao,

Wang

et al. 2024

Electronics

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Due to the rapid development of low earth orbit satellite constellations, e.g., Starlink, OneWeb, etc., integrated satellite-terrestrial networks have been viewed as a promising paradigm to globally provide satellite internet services for users. However, when the contents from ground data centers are provided for users by satellite networks, there will be high capital expenditures in terms of communication delay and bandwidth usage. To this end, in this paper, a cooperative-caching and resource-allocation problem is investigated in integrated satellite–terrestrial networks. Popular contents, which are cached on satellites and ground data centers, can be accessed via inter-satellite and satellite–terrestrial networks in a cooperative way. The optimization problem is formulated to jointly minimize the deployment costs of storage resource usage and network bandwidth consumption. A cooperative caching and resource allocation (CCRA) algorithm based on a neighborhood search is proposed to address the problem. The simulation results demonstrate that the proposed CCRA algorithm outperforms Greedy and BFS in reducing the deployment costs.

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