# Energy-Efficient and QoS-Aware Computation Offloading in GEO/LEO Hybrid Satellite Networks
###### Abstract  
Benefiting from advanced satellite payload technologies, edge computing servers can be deployed on satellites to achieve orbital computing and reduce the mission processing delay. However, geostationary Earth orbit (GEO) satellites are hindered by long-distance communication, whereas low Earth orbit (LEO) satellites are restricted by time windows. Relying solely on GEO or LEO satellites cannot meet the strict quality of service (QoS) requirements of on-board missions while conserving energy consumption. In this paper, we propose a computation offloading strategy for GEO/LEO hybrid satellite networks that minimizes total energy consumption while guaranteeing the QoS requirements of multiple missions. We first innovatively transform the on-board partial computation offloading problem, which is a mixed-integer nonlinear programming (MINLP) problem, into a minimum cost maximum flow (MCMF) problem. Then, the successive shortest path-based computation offloading (SSPCO) method is introduced to obtain the offloading decision in polynomial time. To evaluate the effectiveness and performance of SSPCO, we conduct a series of numerical experiments and compare SSPCO with other offloading methods. The experimental results demonstrate that our proposed SSPCO outperforms the reference methods in terms of total energy consumption, QoS violation degree, and algorithm running time.  
GEO/LEO hybrid satellite networks; computation offloading; energy consumption; quality of service; minimum cost maximum flow +
Footnote †: journal: remote sensing  
0202315329990 center to offload computation tasks, which can effectively reduce the task processing delay and energy consumption of end users [12]. Satellites, due to their ability to provide coverage in special natural spaces such as airspace, land, and sea, are considered promising supplements to enhance the terrestrial networks. Therefore, the increasing demand for real-time and reliable applications has led to the development of OEC technology, which deploys multi-access edge computing (MEC) servers on satellite constellations [6; 13]. [PERSON] et al. [14] proposed a satellite-UAV-served Internet of Things (IoT) system, which utilized satellites to offload tasks to UAV-based edge servers or remote cloud servers for providing seamless coverage to IoT devices in remote areas. Considering the computing power of satellites, [PERSON] et al. [7] presented a hybrid cloud and LEO satellite computation architecture and proposed an offloading algorithm based on the alternating direction method of multipliers to minimize the total energy consumption of users. However, the time window problem of LEO satellites significantly limits the satellites' computing power and mission processing delay [15]. Therefore, GEO/LEO hybrid satellite networks have been a growing interest in recent years due to the advantage of the always-on connectivity of GEO satellites [16; 17; 18]. [PERSON] et al. [19] proposed a collaborative satellite-terrestrial edge computing (STEC) network architecture based on multi-agent collaboration, where GEO satellites serve as the manager covering LEO satellites and connecting to ground cloud servers, while LEO satellites primarily provide in-orbit computing services. To exploit the capability of in-orbit computing fully, [PERSON] et al. [20] developed a satellite network edge computing scenario utilizing a double-layer network topology consisting of LEO and GEO satellites. The offloading decision problem was formulated as a two-sided matching game problem with LEO and GEO satellites as participants. Based on previous works, in this paper, we focus on the latest GEO/LEO hybrid satellite networks and investigate the computation offloading problem in OEC. Table 1 shows the comparison results of our work and other works. The detailed motivations and contributions will be discussed below.