D2D assisted cooperative computational offloading strategy in edge cloud computing networks
Abstract In the computational offloading problem of edge cloud computing (ECC), almost all researches develop the offloading strategy by optimizing the user cost, but most of them only consider the delay and energy consumption, and seldom consider the task waiting delay. This is very unfavorable for...
Saved in:
| Main Authors: | , , , , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
Nature Portfolio
2025-04-01
|
| Series: | Scientific Reports |
| Online Access: | https://doi.org/10.1038/s41598-025-96719-8 |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1850201409874558976 |
|---|---|
| author | Yanyan Wang Dechuan Kong Haojie Chai Hongzhou Qiu Ran Xue Shuhang Li |
| author_facet | Yanyan Wang Dechuan Kong Haojie Chai Hongzhou Qiu Ran Xue Shuhang Li |
| author_sort | Yanyan Wang |
| collection | DOAJ |
| description | Abstract In the computational offloading problem of edge cloud computing (ECC), almost all researches develop the offloading strategy by optimizing the user cost, but most of them only consider the delay and energy consumption, and seldom consider the task waiting delay. This is very unfavorable for tasks with high sensitive latency requirements in the current era of intelligence. In this paper, by using D2D (Device-to-Device) technology, we propose a D2D-assisted collaboration computational offloading strategy (D-CCO) based on user cost optimization to obtain the offloading decision and the number of tasks that can be offloaded. Specifically, we first build a task queue system with multiple local devices, peer devices and edge processors, and compare the execution performance of computing tasks on different devices, taking into account user costs such as task delay, power consumption, and wait delay. Then, the stochastic optimization algorithm and the back-pressure algorithm are used to develop the offloading strategy, which ensures the stability of the system and reduces the computing cost to the greatest extent, so as to obtain the optimal offloading decision. In addition, the stability of the proposed algorithm is analyzed theoretically, that is, the upper bounds of all queues in the system are derived. The simulation results show the stability of the proposed algorithm, and demonstrate that the D-CCO algorithm is superior to other alternatives. Compared with other algorithms, this algorithm can effectively reduce the user cost. |
| format | Article |
| id | doaj-art-75718c7aa19f49bab218dddb5d9b7e57 |
| institution | OA Journals |
| issn | 2045-2322 |
| language | English |
| publishDate | 2025-04-01 |
| publisher | Nature Portfolio |
| record_format | Article |
| series | Scientific Reports |
| spelling | doaj-art-75718c7aa19f49bab218dddb5d9b7e572025-08-20T02:12:01ZengNature PortfolioScientific Reports2045-23222025-04-0115111510.1038/s41598-025-96719-8D2D assisted cooperative computational offloading strategy in edge cloud computing networksYanyan Wang0Dechuan Kong1Haojie Chai2Hongzhou Qiu3Ran Xue4Shuhang Li5School of Artificial Intelligence, Henan Institute of Science and TechnologySchool of Artificial Intelligence, Henan Institute of Science and TechnologySchool of Artificial Intelligence, Henan Institute of Science and TechnologySchool of Artificial Intelligence, Henan Institute of Science and TechnologySchool of Artificial Intelligence, Henan Institute of Science and TechnologySchool of Artificial Intelligence, Henan Institute of Science and TechnologyAbstract In the computational offloading problem of edge cloud computing (ECC), almost all researches develop the offloading strategy by optimizing the user cost, but most of them only consider the delay and energy consumption, and seldom consider the task waiting delay. This is very unfavorable for tasks with high sensitive latency requirements in the current era of intelligence. In this paper, by using D2D (Device-to-Device) technology, we propose a D2D-assisted collaboration computational offloading strategy (D-CCO) based on user cost optimization to obtain the offloading decision and the number of tasks that can be offloaded. Specifically, we first build a task queue system with multiple local devices, peer devices and edge processors, and compare the execution performance of computing tasks on different devices, taking into account user costs such as task delay, power consumption, and wait delay. Then, the stochastic optimization algorithm and the back-pressure algorithm are used to develop the offloading strategy, which ensures the stability of the system and reduces the computing cost to the greatest extent, so as to obtain the optimal offloading decision. In addition, the stability of the proposed algorithm is analyzed theoretically, that is, the upper bounds of all queues in the system are derived. The simulation results show the stability of the proposed algorithm, and demonstrate that the D-CCO algorithm is superior to other alternatives. Compared with other algorithms, this algorithm can effectively reduce the user cost.https://doi.org/10.1038/s41598-025-96719-8 |
| spellingShingle | Yanyan Wang Dechuan Kong Haojie Chai Hongzhou Qiu Ran Xue Shuhang Li D2D assisted cooperative computational offloading strategy in edge cloud computing networks Scientific Reports |
| title | D2D assisted cooperative computational offloading strategy in edge cloud computing networks |
| title_full | D2D assisted cooperative computational offloading strategy in edge cloud computing networks |
| title_fullStr | D2D assisted cooperative computational offloading strategy in edge cloud computing networks |
| title_full_unstemmed | D2D assisted cooperative computational offloading strategy in edge cloud computing networks |
| title_short | D2D assisted cooperative computational offloading strategy in edge cloud computing networks |
| title_sort | d2d assisted cooperative computational offloading strategy in edge cloud computing networks |
| url | https://doi.org/10.1038/s41598-025-96719-8 |
| work_keys_str_mv | AT yanyanwang d2dassistedcooperativecomputationaloffloadingstrategyinedgecloudcomputingnetworks AT dechuankong d2dassistedcooperativecomputationaloffloadingstrategyinedgecloudcomputingnetworks AT haojiechai d2dassistedcooperativecomputationaloffloadingstrategyinedgecloudcomputingnetworks AT hongzhouqiu d2dassistedcooperativecomputationaloffloadingstrategyinedgecloudcomputingnetworks AT ranxue d2dassistedcooperativecomputationaloffloadingstrategyinedgecloudcomputingnetworks AT shuhangli d2dassistedcooperativecomputationaloffloadingstrategyinedgecloudcomputingnetworks |