Synergistic task-offloading in 6G edge networks based on propagation dynamics
In future 6G edge networks, Device-to-Device (D2D)-assisted Mobile Edge Computing (MEC) can fully utilize the idle resources of user terminals (UT) and alleviate the burden on backhaul links. However, the limited idle resources of UT and the over-reliance on D2D-assisted computation offloading may r...
Saved in:
| Main Authors: | , , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
Frontiers Media S.A.
2025-07-01
|
| Series: | Frontiers in Physics |
| Subjects: | |
| Online Access: | https://www.frontiersin.org/articles/10.3389/fphy.2025.1629142/full |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1849421602478882816 |
|---|---|
| author | Chao Zhu Yuexia Zhang Xinyi Wang Xuzhen Zhu |
| author_facet | Chao Zhu Yuexia Zhang Xinyi Wang Xuzhen Zhu |
| author_sort | Chao Zhu |
| collection | DOAJ |
| description | In future 6G edge networks, Device-to-Device (D2D)-assisted Mobile Edge Computing (MEC) can fully utilize the idle resources of user terminals (UT) and alleviate the burden on backhaul links. However, the limited idle resources of UT and the over-reliance on D2D-assisted computation offloading may result in a large number of terminals experiencing task overload, which could lead to the risk of edge network paralysis. To address these issues, this paper establishes a Service-Auxiliary-Request-Healing (SARH) task-offloading propagation model based on propagation dynamics theory. This model describes the dynamic transmission process of offloaded tasks in 6G edge networks and constructs two linear threshold functions to characterize the differences in task processing capabilities between UT and edge servers (ES). Furthermore, the proposed task-offloading propagation model is theoretically analyzed using edge compartment theory, and the propagation dynamics equations are established to derive the saddle point and critical conditions leading to task overload in a large number of UT, providing theoretical guidance for preventing network paralysis. Finally, simulation results show that the SARH model effectively describes the task-offloading propagation process in edge networks, and by controlling key factors such as the proportion of UT selecting D2D-assisted MEC synergistic task-offloading, network connectivity density, and network degree distribution heterogeneity, network paralysis can be avoided. |
| format | Article |
| id | doaj-art-a5ceca3cfde54e9a85674a2992bd3752 |
| institution | Kabale University |
| issn | 2296-424X |
| language | English |
| publishDate | 2025-07-01 |
| publisher | Frontiers Media S.A. |
| record_format | Article |
| series | Frontiers in Physics |
| spelling | doaj-art-a5ceca3cfde54e9a85674a2992bd37522025-08-20T03:31:24ZengFrontiers Media S.A.Frontiers in Physics2296-424X2025-07-011310.3389/fphy.2025.16291421629142Synergistic task-offloading in 6G edge networks based on propagation dynamicsChao Zhu0Yuexia Zhang1Xinyi Wang2Xuzhen Zhu3Key Laboratory of Information and Communication Systems, Ministry of Information Industry, Beijing Information Science and Technology University, Beijing, ChinaKey Laboratory of Modern Measurement and Control Technology, Ministry of Education, Beijing Information Science and Technology University, Beijing, ChinaKey Laboratory of Information and Communication Systems, Ministry of Information Industry, Beijing Information Science and Technology University, Beijing, ChinaState Key Laboratory of Networking and Switching Technology, Beijing University of Posts and Telecommunications, Beijing, ChinaIn future 6G edge networks, Device-to-Device (D2D)-assisted Mobile Edge Computing (MEC) can fully utilize the idle resources of user terminals (UT) and alleviate the burden on backhaul links. However, the limited idle resources of UT and the over-reliance on D2D-assisted computation offloading may result in a large number of terminals experiencing task overload, which could lead to the risk of edge network paralysis. To address these issues, this paper establishes a Service-Auxiliary-Request-Healing (SARH) task-offloading propagation model based on propagation dynamics theory. This model describes the dynamic transmission process of offloaded tasks in 6G edge networks and constructs two linear threshold functions to characterize the differences in task processing capabilities between UT and edge servers (ES). Furthermore, the proposed task-offloading propagation model is theoretically analyzed using edge compartment theory, and the propagation dynamics equations are established to derive the saddle point and critical conditions leading to task overload in a large number of UT, providing theoretical guidance for preventing network paralysis. Finally, simulation results show that the SARH model effectively describes the task-offloading propagation process in edge networks, and by controlling key factors such as the proportion of UT selecting D2D-assisted MEC synergistic task-offloading, network connectivity density, and network degree distribution heterogeneity, network paralysis can be avoided.https://www.frontiersin.org/articles/10.3389/fphy.2025.1629142/full6G edge networkspropagation dynamicsD2Dtask-offloadingevolution mechanism |
| spellingShingle | Chao Zhu Yuexia Zhang Xinyi Wang Xuzhen Zhu Synergistic task-offloading in 6G edge networks based on propagation dynamics Frontiers in Physics 6G edge networks propagation dynamics D2D task-offloading evolution mechanism |
| title | Synergistic task-offloading in 6G edge networks based on propagation dynamics |
| title_full | Synergistic task-offloading in 6G edge networks based on propagation dynamics |
| title_fullStr | Synergistic task-offloading in 6G edge networks based on propagation dynamics |
| title_full_unstemmed | Synergistic task-offloading in 6G edge networks based on propagation dynamics |
| title_short | Synergistic task-offloading in 6G edge networks based on propagation dynamics |
| title_sort | synergistic task offloading in 6g edge networks based on propagation dynamics |
| topic | 6G edge networks propagation dynamics D2D task-offloading evolution mechanism |
| url | https://www.frontiersin.org/articles/10.3389/fphy.2025.1629142/full |
| work_keys_str_mv | AT chaozhu synergistictaskoffloadingin6gedgenetworksbasedonpropagationdynamics AT yuexiazhang synergistictaskoffloadingin6gedgenetworksbasedonpropagationdynamics AT xinyiwang synergistictaskoffloadingin6gedgenetworksbasedonpropagationdynamics AT xuzhenzhu synergistictaskoffloadingin6gedgenetworksbasedonpropagationdynamics |