Resource scheduling optimization in cloud-edge collaboration
With the enrichment and diversification of business types, low latency, high bandwidth, data privacy and high reliability have become common requirements.Edge computing, fog computing, distributed cloud, computing power network and other solutions have been proposed, and have triggered in-depth rese...
Saved in:
| Main Authors: | , , , |
|---|---|
| Format: | Article |
| Language: | zho |
| Published: |
Beijing Xintong Media Co., Ltd
2023-02-01
|
| Series: | Dianxin kexue |
| Subjects: | |
| Online Access: | http://www.telecomsci.com/zh/article/doi/10.11959/j.issn.1000-0801.2023027/ |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1841530860423086080 |
|---|---|
| author | Shuling WANG Jie SUN Peng WANG Aidong YANG |
| author_facet | Shuling WANG Jie SUN Peng WANG Aidong YANG |
| author_sort | Shuling WANG |
| collection | DOAJ |
| description | With the enrichment and diversification of business types, low latency, high bandwidth, data privacy and high reliability have become common requirements.Edge computing, fog computing, distributed cloud, computing power network and other solutions have been proposed, and have triggered in-depth research and exploration in industry, academia and research.There is a consensus within and outside the industry on the view that “multi-level computing power distribution and collaboration of computing power will be the mainstream of computing power structure in the future”.The problems related to resource scheduling optimization, such as computing power management, allocation, scheduling, have also become the current research hotspot and key research direction.Therefore, for the future computing power supply structure, focuses on the latest progress of resource scheduling optimization in academia and industry, the current main methodology and engineering implementation architecture was summarized.And then, for the two typical cloud edge collaboration scenarios, the analysis was carried out from the perspective of scene splitting, scheduling objectives, and solutions in turn, and the resource scheduling optimization reference schemes that adapted to the characteristics of the scenarios were analyzed and discussed respectively. |
| format | Article |
| id | doaj-art-c3bbea4b45024970afebf170225ced67 |
| institution | Kabale University |
| issn | 1000-0801 |
| language | zho |
| publishDate | 2023-02-01 |
| publisher | Beijing Xintong Media Co., Ltd |
| record_format | Article |
| series | Dianxin kexue |
| spelling | doaj-art-c3bbea4b45024970afebf170225ced672025-01-15T02:59:07ZzhoBeijing Xintong Media Co., LtdDianxin kexue1000-08012023-02-013916317059570884Resource scheduling optimization in cloud-edge collaborationShuling WANGJie SUNPeng WANGAidong YANGWith the enrichment and diversification of business types, low latency, high bandwidth, data privacy and high reliability have become common requirements.Edge computing, fog computing, distributed cloud, computing power network and other solutions have been proposed, and have triggered in-depth research and exploration in industry, academia and research.There is a consensus within and outside the industry on the view that “multi-level computing power distribution and collaboration of computing power will be the mainstream of computing power structure in the future”.The problems related to resource scheduling optimization, such as computing power management, allocation, scheduling, have also become the current research hotspot and key research direction.Therefore, for the future computing power supply structure, focuses on the latest progress of resource scheduling optimization in academia and industry, the current main methodology and engineering implementation architecture was summarized.And then, for the two typical cloud edge collaboration scenarios, the analysis was carried out from the perspective of scene splitting, scheduling objectives, and solutions in turn, and the resource scheduling optimization reference schemes that adapted to the characteristics of the scenarios were analyzed and discussed respectively.http://www.telecomsci.com/zh/article/doi/10.11959/j.issn.1000-0801.2023027/cloud-edge collaborationedge computingcomputer and network convergenceresource scheduling optimizationcomputing and networking joint optimization |
| spellingShingle | Shuling WANG Jie SUN Peng WANG Aidong YANG Resource scheduling optimization in cloud-edge collaboration Dianxin kexue cloud-edge collaboration edge computing computer and network convergence resource scheduling optimization computing and networking joint optimization |
| title | Resource scheduling optimization in cloud-edge collaboration |
| title_full | Resource scheduling optimization in cloud-edge collaboration |
| title_fullStr | Resource scheduling optimization in cloud-edge collaboration |
| title_full_unstemmed | Resource scheduling optimization in cloud-edge collaboration |
| title_short | Resource scheduling optimization in cloud-edge collaboration |
| title_sort | resource scheduling optimization in cloud edge collaboration |
| topic | cloud-edge collaboration edge computing computer and network convergence resource scheduling optimization computing and networking joint optimization |
| url | http://www.telecomsci.com/zh/article/doi/10.11959/j.issn.1000-0801.2023027/ |
| work_keys_str_mv | AT shulingwang resourceschedulingoptimizationincloudedgecollaboration AT jiesun resourceschedulingoptimizationincloudedgecollaboration AT pengwang resourceschedulingoptimizationincloudedgecollaboration AT aidongyang resourceschedulingoptimizationincloudedgecollaboration |