Research on task offloading algorithm of mobile edge computing based on deep reinforcement learning in SDCN
With the continuous development of network technology, the network topology distributed network control mode based on Fat-Tree gradually reveals its limitations.Software-defined data center network (SDCN) technology, as an improved technology of Fat-Tree network topology, has attracted more and more...
Saved in:
| Main Authors: | , |
|---|---|
| Format: | Article |
| Language: | zho |
| Published: |
Beijing Xintong Media Co., Ltd
2024-02-01
|
| Series: | Dianxin kexue |
| Subjects: | |
| Online Access: | http://www.telecomsci.com/zh/article/doi/10.11959/j.issn.1000-0801.2024025/ |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1841533934834286592 |
|---|---|
| author | Shouhua JIANG Yiwu WANG |
| author_facet | Shouhua JIANG Yiwu WANG |
| author_sort | Shouhua JIANG |
| collection | DOAJ |
| description | With the continuous development of network technology, the network topology distributed network control mode based on Fat-Tree gradually reveals its limitations.Software-defined data center network (SDCN) technology, as an improved technology of Fat-Tree network topology, has attracted more and more researchers’ attention.Firstly, an edge computing architecture in SDCN and a task offloading model based on the three-layer service architecture of the mobile edge computing (MEC) platform were built, combined with the actual application scenarios of the MEC platform.Through the same strategy experience playback and entropy regularization, the traditional deep Q-leaning network (DQN) algorithm was improved, and the task offloading strategy of MEC platform was optimized.An improved DQN algorithm based on same strategy empirical playback and entropy regularization (RSS2E-DQN) was compared with three other algorithms in load balancing, energy consumption, delay and network usage.It is verified that the improved algorithm has better performance in the above four aspects. |
| format | Article |
| id | doaj-art-d2eae87d1d024d6585dd32588e2e199d |
| institution | Kabale University |
| issn | 1000-0801 |
| language | zho |
| publishDate | 2024-02-01 |
| publisher | Beijing Xintong Media Co., Ltd |
| record_format | Article |
| series | Dianxin kexue |
| spelling | doaj-art-d2eae87d1d024d6585dd32588e2e199d2025-01-15T02:48:35ZzhoBeijing Xintong Media Co., LtdDianxin kexue1000-08012024-02-01409610659555963Research on task offloading algorithm of mobile edge computing based on deep reinforcement learning in SDCNShouhua JIANGYiwu WANGWith the continuous development of network technology, the network topology distributed network control mode based on Fat-Tree gradually reveals its limitations.Software-defined data center network (SDCN) technology, as an improved technology of Fat-Tree network topology, has attracted more and more researchers’ attention.Firstly, an edge computing architecture in SDCN and a task offloading model based on the three-layer service architecture of the mobile edge computing (MEC) platform were built, combined with the actual application scenarios of the MEC platform.Through the same strategy experience playback and entropy regularization, the traditional deep Q-leaning network (DQN) algorithm was improved, and the task offloading strategy of MEC platform was optimized.An improved DQN algorithm based on same strategy empirical playback and entropy regularization (RSS2E-DQN) was compared with three other algorithms in load balancing, energy consumption, delay and network usage.It is verified that the improved algorithm has better performance in the above four aspects.http://www.telecomsci.com/zh/article/doi/10.11959/j.issn.1000-0801.2024025/software-defined data center networkdeep reinforcement learningedge computing task offloadingreplay the same strategy experienceentropy regularity |
| spellingShingle | Shouhua JIANG Yiwu WANG Research on task offloading algorithm of mobile edge computing based on deep reinforcement learning in SDCN Dianxin kexue software-defined data center network deep reinforcement learning edge computing task offloading replay the same strategy experience entropy regularity |
| title | Research on task offloading algorithm of mobile edge computing based on deep reinforcement learning in SDCN |
| title_full | Research on task offloading algorithm of mobile edge computing based on deep reinforcement learning in SDCN |
| title_fullStr | Research on task offloading algorithm of mobile edge computing based on deep reinforcement learning in SDCN |
| title_full_unstemmed | Research on task offloading algorithm of mobile edge computing based on deep reinforcement learning in SDCN |
| title_short | Research on task offloading algorithm of mobile edge computing based on deep reinforcement learning in SDCN |
| title_sort | research on task offloading algorithm of mobile edge computing based on deep reinforcement learning in sdcn |
| topic | software-defined data center network deep reinforcement learning edge computing task offloading replay the same strategy experience entropy regularity |
| url | http://www.telecomsci.com/zh/article/doi/10.11959/j.issn.1000-0801.2024025/ |
| work_keys_str_mv | AT shouhuajiang researchontaskoffloadingalgorithmofmobileedgecomputingbasedondeepreinforcementlearninginsdcn AT yiwuwang researchontaskoffloadingalgorithmofmobileedgecomputingbasedondeepreinforcementlearninginsdcn |