Joint Offloading Decision and Resource Allocation in Mobile Edge Computing-Enabled Satellite-Terrestrial Network

Tong, Minglei; Wang, Xiaoxiang; Li, Song; Peng, Lei

doi:10.3390/sym14030564

Cited by 19 publications

(7 citation statements)

References 33 publications

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“…Ref. [29] proposed a multi-index joint optimization task-offloading strategy based on game theory, which can effectively improve service quality. All these works can make reasonable offloading decisions, but they all ignore the possible privacy leakage problem during the offloading process.…”

Section: Task Offloading In Satellite-terrestrial Cooperative Networkmentioning

confidence: 99%

Privacy-Friendly Task Offloading for Smart Grid in 6G Satellite–Terrestrial Edge Computing Networks

Zou,

Yuan,

Xin

et al. 2023

Electronics

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Through offloading computing tasks to visible satellites for execution, the satellite edge computing architecture effectively issues the high-delay problem in remote grids (e.g., mountain and desert) when tasks are offloaded to the urban terrestrial cloud (TC). However, existing works are usually limited to offloading tasks in pure satellite networks and make offloading decisions based on the predefined models. Additionally, runtime consumption for offloading decisions is rather high. Furthermore, privacy information may be maliciously sniffed since computing tasks are transmitted via vulnerable satellite networks. In this paper, we study the task-offloading problem in satellite–terrestrial edge computing networks, where tasks can be executed by satellite or urban TC. A privacy leakage scenario is described, and we consider preserving privacy by sending extra random dummy tasks to confuse adversaries. Then, the offloading cost with privacy protection consideration is modeled, and the offloading decision that minimizes the offloading cost is formulated as a mixed-integer programming (MIP) problem. To speed up solving the MIP problem, we propose a deep reinforcement learning-based task-offloading (DRTO) algorithm. In this case, offloading location and bandwidth allocation only depend on the current channel states. Simulation results show that the offloading overhead is reduced by 17.5% and 23.6% compared with pure TC computing and pure SatEC computing, while the runtime consumption of DRTO is reduced by at least 42.6%. The dummy tasks are exhibited to effectively mitigate privacy leakage during offloading.

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Section: Task Offloading In Satellite-terrestrial Cooperative Networkmentioning

confidence: 99%

Privacy-Friendly Task Offloading for Smart Grid in 6G Satellite–Terrestrial Edge Computing Networks

Zou,

Yuan,

Xin

et al. 2023

Electronics

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“…Pfandzelter et al [13] proposed a resource place method to select a subset of satellites as computing nodes with QoS constraints. A joint offloading decision and resource allocation scheme is designed in [14] considering satellites as the computing node to assist the terrestrial computing network in task processing, which minimizes the completion delay of computing tasks.…”

Section: Related Workmentioning

confidence: 99%

A Novel Decomposed Optical Architecture for Satellite Terrestrial Network Edge Computing

et al. 2022

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Aiming at providing a high-performance terrestrial network for edge computing in satellite networks, we experimentally demonstrate a high bandwidth and low latency decomposed optical computing architecture based on distributed Nanoseconds Optical Switches (NOS). Experimental validation of the decomposed computing network prototype employs a four-port NOS to interconnect four processor/memory cubes. The SOA-based optical gates provide an ON/OFF ratio greater than 60 dB, enabling none-error transmission at a Bit Error Rate (BER) of 1 × 10−9. An end-to-end access latency of 122.3 ns and zero packet loss are obtained in the experimental assessment. Scalability and physical performance considering signal impairments when increasing the NOS port count are also investigated. An output OSNR of up to 30.5 dB and an none-error transmission with 1.5 dB penalty is obtained when scaling the NOS port count to 64. Moreover, exploiting the experimentally measured parameters, the network performance of NOS-based decomposed computing architecture is numerically assessed under larger network scales. The results indicate that, under a 4096-cube network scale, the NOS-based decomposed computing architecture achieves 148.5 ns end-to-end latency inside the same rack and zero packet loss at a link bandwidth of 40 Gb/s.

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“…A computational unloading algorithm using alternating direction multiplier method is proposed. In literature [11], a scheme based on potential game and Lagrange multiplier method is proposed to obtain unloading decision and computing resource allocation under the satellite cloud-edge collaboration scenario. However, this paper takes the delay cost of ground users as the optimization objective, without considering the energy consumption of ground users.…”

Section: Introductionmentioning

confidence: 99%

Cloud-edge collaborative computing offloading and resource allocation optimization in LEO satellite network

Tian,

Sun

2024

ACETR

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Satellite edge computing can provide communication and computing services to areas lacking ground network coverage, enhancing the computational capabilities of terrestrial users. However, due to the relatively limited computational resources available on satellites, cloud-edge collaborative computing in Low Earth Orbit (LEO) satellite communication scenarios emerges as a more optimal solution. In this paper, a cloud-edge collaborative offloading algorithm based on Deep Deterministic Policy Gradient (DDPG) algorithm is proposed to solve the offloading decision and resource allocation problems. The optimization problem is modeled as Markov decision process, and the comprehensive delay and energy consumption cost of computing tasks completed by ground users are optimized. Simulation results validate that the proposed algorithm has good convergence properties and lower comprehensive cost than processing all tasks locally and offloading all tasks to LEO satellites.

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Joint Offloading Decision and Resource Allocation in Mobile Edge Computing-Enabled Satellite-Terrestrial Network

Cited by 19 publications

References 33 publications

Privacy-Friendly Task Offloading for Smart Grid in 6G Satellite–Terrestrial Edge Computing Networks

Privacy-Friendly Task Offloading for Smart Grid in 6G Satellite–Terrestrial Edge Computing Networks

A Novel Decomposed Optical Architecture for Satellite Terrestrial Network Edge Computing

Cloud-edge collaborative computing offloading and resource allocation optimization in LEO satellite network

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