东北大学学报（自然科学版）

东北大学学报（自然科学版） ›› 2022, Vol. 43 ›› Issue (3): 376-383.DOI: 10.12068/j.issn.1005-3026.2022.03.010

• 信息与控制 • 上一篇    下一篇

面向天地一体化网络的计算卸载算法

耿蓉1， 王宏艳1， 刘畅1， 徐赛2   

  1. (1. 东北大学 计算机科学与工程学院， 辽宁 沈阳110169; 2. 东软集团股份有限公司， 辽宁 沈阳110179)
  • 修回日期:2021-05-19 接受日期:2021-05-19 发布日期:2022-05-18
  • 通讯作者: 耿蓉
  • 作者简介:耿蓉(1979-)，女，辽宁沈阳人，东北大学副教授.
  • 基金资助:
    国家自然科学基金资助项目(61671141); 中央高校基本科研业务费专项资金资助项目(N2116015，N2116020).

Computational Offloading Algorithm Oriented to the Space-Earth Integration Network

GENG Rong1， WANG Hong-yan1， LIU Chang1， XU Sai2   

  1. 1. School of Computer Science & Engineering， Northeastern University， Shenyang 110169， China; 2. Neusoft Corporation， Shenyang 110179， China.
  • Revised:2021-05-19 Accepted:2021-05-19 Published:2022-05-18
  • Contact: WANG Hong-yan
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摘要： 由于天地一体化网络的计算资源受限、能力迥异等问题，会导致其处理复杂任务的能力减弱，使得重要的任务处理失败.因此，本文构建了一种将任务卸载到本地-骨干-边缘接入节点的三层计算卸载开销模型，并通过基于DQN的最优卸载算法进行最优卸载策略的制定.首先，依据网络中存在的天基骨干节点、边缘接入节点以及地基骨干节点三种类型计算节点(卸载站点)自身的特点，给出了不同卸载站点的时延、能耗的开销表达式以及对应的约束条件. 然后，提出了基于DQN算法来完成低时延、低能耗的卸载过程.仿真结果表明，DQN算法能够提高任务执行的速度，降低终端设备的能耗，有效改善网络中计算节点资源迥异的现状.

关键词: 天地一体化网络;计算卸载;卸载开销模型;时延;能耗;DQN

Abstract: Due to the limited computing resources and very different capabilities of the space-earth integration network， the processing power of complex tasks is not strong and important tasks fail to be processed. Therefore， a three-layer computational offloading overhead model that offloads tasks to local-backbone-edge access nodes was established and the optimal offloading strategy was formulated through the optimal offloading algorithm based on DQN(deep Q-learning network). Firstly， based on the characteristics of the three types of computing nodes(offloading sites)in the network， including the space-based backbone nodes， edge access nodes and ground-based backbone nodes， the expressions of the delay， energy consumption and the corresponding expressions of different offload sites were given. Then， based on this， the DQN algorithm was proposed to complete the low-delay， low-energy offloading process. Finally， simulation results show that the DQN algorithm can improve the speed of task execution， reduce the energy consumption of terminal equipment， and effectively improve the current situation of computing node resources in the network.

Key words: space-earth integration network; computational offloading; offloading overhead model; delay; energy consumption; DQN(deep Q-learning network)

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