Research on Task-Oriented Computation Offloading Decision in Space-Air-Ground Integrated Network
In Space–Air–Ground Integrated Networks (SAGIN), computation offloading technology is a new way to improve the processing efficiency of node tasks and improve the limitation of computing storage resources. To solve the problem of large delay and energy consumption cost of task computation offloading...
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| Format: | Article |
| Language: | English |
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MDPI AG
2021-05-01
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| Series: | Future Internet |
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| Online Access: | https://www.mdpi.com/1999-5903/13/5/128 |
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| author | Jun Liu Xiaohui Lian Chang Liu |
| author_facet | Jun Liu Xiaohui Lian Chang Liu |
| author_sort | Jun Liu |
| collection | DOAJ |
| description | In Space–Air–Ground Integrated Networks (SAGIN), computation offloading technology is a new way to improve the processing efficiency of node tasks and improve the limitation of computing storage resources. To solve the problem of large delay and energy consumption cost of task computation offloading, which caused by the complex and variable network offloading environment and a large amount of offloading tasks, a computation offloading decision scheme based on Markov and Deep Q Networks (DQN) is proposed. First, we select the optimal offloading network based on the characteristics of the movement of the task offloading process in the network. Then, the task offloading process is transformed into a Markov state transition process to build a model of the computational offloading decision process. Finally, the delay and energy consumption weights are introduced into the DQN algorithm to update the computation offloading decision process, and the optimal offloading decision under the low cost is achieved according to the task attributes. The simulation results show that compared with the traditional Lyapunov-based offloading decision scheme and the classical Q-learning algorithm, the delay and energy consumption are respectively reduced by 68.33% and 11.21%, under equal weights when the offloading task volume exceeds 500 Mbit. Moreover, compared with offloading to edge nodes or backbone nodes of the network alone, the proposed mixed offloading model can satisfy more than 100 task requests with low energy consumption and low delay. It can be seen that the computation offloading decision proposed in this paper can effectively reduce the delay and energy consumption during the task computation offloading in the Space–Air–Ground Integrated Network environment, and can select the optimal offloading sites to execute the tasks according to the characteristics of the task itself. |
| first_indexed | 2024-03-10T11:26:25Z |
| format | Article |
| id | doaj.art-036016a503e5482499fea7f74381250b |
| institution | Directory Open Access Journal |
| issn | 1999-5903 |
| language | English |
| last_indexed | 2024-03-10T11:26:25Z |
| publishDate | 2021-05-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Future Internet |
| spelling | doaj.art-036016a503e5482499fea7f74381250b2023-11-21T19:34:20ZengMDPI AGFuture Internet1999-59032021-05-0113512810.3390/fi13050128Research on Task-Oriented Computation Offloading Decision in Space-Air-Ground Integrated NetworkJun Liu0Xiaohui Lian1Chang Liu2School of Computer Science and Engineering, Northeastern University, Shenyang 110169, ChinaSchool of Computer Science and Engineering, Northeastern University, Shenyang 110169, ChinaSchool of Computer Science and Engineering, Northeastern University, Shenyang 110169, ChinaIn Space–Air–Ground Integrated Networks (SAGIN), computation offloading technology is a new way to improve the processing efficiency of node tasks and improve the limitation of computing storage resources. To solve the problem of large delay and energy consumption cost of task computation offloading, which caused by the complex and variable network offloading environment and a large amount of offloading tasks, a computation offloading decision scheme based on Markov and Deep Q Networks (DQN) is proposed. First, we select the optimal offloading network based on the characteristics of the movement of the task offloading process in the network. Then, the task offloading process is transformed into a Markov state transition process to build a model of the computational offloading decision process. Finally, the delay and energy consumption weights are introduced into the DQN algorithm to update the computation offloading decision process, and the optimal offloading decision under the low cost is achieved according to the task attributes. The simulation results show that compared with the traditional Lyapunov-based offloading decision scheme and the classical Q-learning algorithm, the delay and energy consumption are respectively reduced by 68.33% and 11.21%, under equal weights when the offloading task volume exceeds 500 Mbit. Moreover, compared with offloading to edge nodes or backbone nodes of the network alone, the proposed mixed offloading model can satisfy more than 100 task requests with low energy consumption and low delay. It can be seen that the computation offloading decision proposed in this paper can effectively reduce the delay and energy consumption during the task computation offloading in the Space–Air–Ground Integrated Network environment, and can select the optimal offloading sites to execute the tasks according to the characteristics of the task itself.https://www.mdpi.com/1999-5903/13/5/128Space–Air–Ground Integrated Networkcomputation offloadingMarkov decision processDQN |
| spellingShingle | Jun Liu Xiaohui Lian Chang Liu Research on Task-Oriented Computation Offloading Decision in Space-Air-Ground Integrated Network Future Internet Space–Air–Ground Integrated Network computation offloading Markov decision process DQN |
| title | Research on Task-Oriented Computation Offloading Decision in Space-Air-Ground Integrated Network |
| title_full | Research on Task-Oriented Computation Offloading Decision in Space-Air-Ground Integrated Network |
| title_fullStr | Research on Task-Oriented Computation Offloading Decision in Space-Air-Ground Integrated Network |
| title_full_unstemmed | Research on Task-Oriented Computation Offloading Decision in Space-Air-Ground Integrated Network |
| title_short | Research on Task-Oriented Computation Offloading Decision in Space-Air-Ground Integrated Network |
| title_sort | research on task oriented computation offloading decision in space air ground integrated network |
| topic | Space–Air–Ground Integrated Network computation offloading Markov decision process DQN |
| url | https://www.mdpi.com/1999-5903/13/5/128 |
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