Optimal bandwidth and computing resource allocation in low earth orbit satellite constellation for earth observation applications

Valente, Francesco; Eramo, V.; Lavacca, Francesco G.

doi:10.1016/j.comnet.2023.109849

Cited by 7 publications

(4 citation statements)

References 16 publications

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“…On the other hand, edge computing technology extends the cloud computing platform to network edge nodes, providing users with multi-layer and heterogeneous computing resources. As a promising paradigm, edge computing is considered a key enabler to further increase the transmission rate and reduce processing delay for integrated satelliteterrestrial networks [12][13][14]. One typical and novel work in [14] addressed the allocation of bandwidth and computation resources in low-Earth-orbit satellite networks for Earth observation applications.…”

Section: Related Workmentioning

confidence: 99%

“…As a promising paradigm, edge computing is considered a key enabler to further increase the transmission rate and reduce processing delay for integrated satelliteterrestrial networks [12][13][14]. One typical and novel work in [14] addressed the allocation of bandwidth and computation resources in low-Earth-orbit satellite networks for Earth observation applications. In this context, the satellites themselves process observation data to avoid transmitting useless data to ground stations and wasting precious bandwidth.…”

Section: Related Workmentioning

confidence: 99%

“…Consequently, algorithms were developed to tackle these interconnected problems. However, the algorithms presented in [14] cannot be directly applied as a benchmark for our work because: (1) Our work focuses on the routing problem for a given set of continuous, infinite-time data transmission flow requests. In contrast, ref.…”

Section: Related Workmentioning

confidence: 99%

“…In contrast, ref. [14] primarily considers routing design for Earth observation data, which typically involves periodic transmission; (2) In [14], the routing request only specifies the source satellite, while the destination is not predetermined and can be any ground station. The destination is determined simultaneously by computation resource allocation.…”

Section: Related Workmentioning

confidence: 99%

See 3 more Smart Citations

Delay-Sensitive Service Provisioning in Software-Defined Low-Earth-Orbit Satellite Networks

Dong

Zhang²,

Liu

et al. 2023

Electronics

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With the advancement of space technology and satellite communications, low-Earth-orbit (LEO) satellite networks have experienced rapid development in the past decade. In the vision of 6G, LEO satellite networks play an important role in future 6G networks. On the other hand, a variety of applications, including many delay-sensitive applications, are continuously emerging. Due to the highly dynamic nature of LEO satellite networks, supporting time-deterministic services in such networks is challenging. However, we can provide latency guarantees for most delay-sensitive applications through data plane traffic shaping and control plane routing optimization. This paper addresses the routing optimization problem for time-sensitive (TS) flows in software-defined low-Earth-orbit (LEO) satellite networks. We model the problem as an integer linear programming (ILP) model aiming to minimize path handovers and maximum link utilization while meeting TS flow latency constraints. Since this problem is NP-hard, we design an efficient longest continuous path (LCP) approximation algorithm. LCP selects the longest valid path in each topology snapshot that satisfies delay constraints. An auxiliary graph then determines the routing sequence with minimized handovers. We implement an LEO satellite network testbed with Open vSwitch (OVS) and an open-network operating system (ONOS) controller to evaluate LCP. The results show that LCP reduces the number of path handovers by up to 31.7% and keeps the maximum link utilization lowest for more than 75% of the time compared to benchmark algorithms. In summary, LCP achieves excellent path handover optimization and load balancing performance under TS flow latency constraints.

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