Application and Performance Evaluation of Resource Pool Architecture in Satellite Edge Computing

Qin, Junxiang; Guo, Xiaojie; Ma, Xiaotian; Li, Xuan; Yang, Jun

doi:10.3390/aerospace9080451

Cited by 3 publications

(4 citation statements)

References 21 publications

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“…The widespread IoT devices motivate the convergence of satellite networks and terrestrial networks. Furthermore, since embedding MEC servers in satellites can provide a computing service for IoT devices, even in remote and depopulated areas, SEC has received extensive attention [10][11][12][13][14].…”

Section: Introductionmentioning

confidence: 99%

“…Aerospace 2023, 10, x FOR PEER REVIEW 2 of 27 Furthermore, since embedding MEC servers in satellites can provide a computing service for IoT devices, even in remote and depopulated areas, SEC has received extensive attention [10][11][12][13][14].…”

Section: Introductionmentioning

confidence: 99%

“…Hu et al [11] proposed a task offloading strategy based on Lyapunov optimization. Qin et al [12] designed a search matching-based algorithm to solve the dynamic satellite selection problem that improves the load balance of satellite resources. Yu et al [13] proposed a deep-imitation-learning-driven offloading and caching algorithm to achieve real-time decision making.…”

Section: Introductionmentioning

confidence: 99%

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Task Offloading with Data-Dependent Constraints in Satellite Edge Computing Networks: A Multi-Objective Approach

Zhang,

Feng,

Yang

et al. 2023

Aerospace

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Senabling a satellite network with edge computing capabilities, SEC provides users with a full range of computing service. In this paper, we construct a multi-objective optimization model for task offloading with data-dependent constraints in an SEC network and aim to achieve optimal tradeoffs among energy consumption, cost, and makespan. However, dependency constraints between tasks may lead to unexpected computational delays and even task failures in an SEC network. To solve this, we proposed a Petri-net-based constraint amending method with polynomial complexity and generated offloading results satisfying our constraints. For the multiple optimization objectives, a strengthened dominance relation sort was established to balance the convergence and diversity of nondominated solutions. Based on these, we designed a multi-objective wolf pack search (MOWPS) algorithm. A series of adaptive mechanisms was employed for avoiding additional computational overhead, and a Lamarckian-learning-based multi-neighborhood search prevents MOWPS from becoming trapped in the local optimum. Extensive computational experiments demonstrate the outperformance of MOWPS for solving task offloading with data-dependent constraints in an SEC network.

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Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Task Offloading with Data-Dependent Constraints in Satellite Edge Computing Networks: A Multi-Objective Approach

Zhang,

Feng,

Yang

et al. 2023

Aerospace

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“…For instance, the US company SpaceX's Starlink constellation is expected to deploy approximately 12,000 satellites by 2027, providing high-speed and low-cost internet services for billions of users worldwide, including those in remote areas such as deserts and mountains [3]. Benefiting from the development of on-board processing technology, orbital edge computing (OEC) has emerged as a focal point of interest for both academic and industrial communities and is being recognized as a key area of research for the implementation of future network infrastructures [4,5]. However, due to the limited computing resources and energy, in OEC, a fundamental problem is designing an efficient computation offloading strategy to coordinate the satellite computing capabilities, thus meeting the processing delay and energy consumption requirements of multiple missions [6,7].…”

Section: Introductionmentioning

confidence: 99%

Energy-Efficient and QoS-Aware Computation Offloading in GEO/LEO Hybrid Satellite Networks

Lv,

Yang,

Ding

et al. 2023

Remote Sensing

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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.

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Towards Multi-Satellite Collaborative Computing via Task Scheduling Based on Genetic Algorithm

et al. 2023

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With satellite systems rapidly developing in multiple satellites, multiple tasks, and high-speed response speed requirements, existing computing techniques face the following challenges: insufficient computing power, limited computing resources, and weaker coordination ability. Meanwhile, most methods have more significant response speed and resource utilization limitations. To solve the above problem, we propose a distributed collaborative computing framework with a genetic algorithm-based task scheduling model (DCCF-GA), which can realize the collaborative computing between multiple satellites through genetic algorithm. Specifically, it contains two aspects of work. First, a distributed architecture of satellites is constructed where the main satellite is responsible for distribution and scheduling, and the computing satellite is accountable for completing the task. Then, we presented a genetic algorithm-based task scheduling model that enables multiple satellites to collaborate for completing the tasks. Experiments show that the proposed algorithm has apparent advantages in completion time and outperforms other algorithms in resource efficiency.

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Application and Performance Evaluation of Resource Pool Architecture in Satellite Edge Computing

Cited by 3 publications

References 21 publications

Task Offloading with Data-Dependent Constraints in Satellite Edge Computing Networks: A Multi-Objective Approach

Task Offloading with Data-Dependent Constraints in Satellite Edge Computing Networks: A Multi-Objective Approach

Energy-Efficient and QoS-Aware Computation Offloading in GEO/LEO Hybrid Satellite Networks

Towards Multi-Satellite Collaborative Computing via Task Scheduling Based on Genetic Algorithm

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