Explore deep reinforcement learning for efficient task processing based on federated optimization in big data

doi:10.1016/j.future.2023.06.027

CQUET-IR > 智慧健康学院

	Explore deep reinforcement learning for efficient task processing based on federated optimization in big data
	Xiao, Shan; Wu, Chunyi
	2023-12
发表期刊	Future Generation Computer Systems-The International Journal of eScience
ISSN	0167-739X
EISSN	1872-7115
卷号	149 页码:150-161
摘要	In recent years, along with the extensive application of consumer electronics, the task execution with cloud computing for big data has become one of the research focuses. Nevertheless, the traditional theories and algorithms are still employed by existing research work to explore the feasible solutions, which takes a beating from low generalization performance, system load imbalance, more response delay, etc. To solve the matter, a task execution method called DROP (Deep Reinforcement network aided Optimization method aiming at task Processing) has been put forward, which is capable of completing task request allocation through virtual network embedding. The prominence of this method is explained by its effect in reducing load balancing degree, minimizing bandwidth resource overhead, and preserving electric energy as well as meeting customer demands. It makes use of Deep Deterministic Policy Gradient (DDPG) instead of depending on tons of iterations for better path selection schemes in previous methods, through continuous environment interaction and trial-and-error evaluation to get better strategy selection for virtual link embedding. To realize the virtual node embedding in the federated optimization based system architecture, the intentional deep feature learning network is applied. Compared with the cutting edge approaches, the performance benefits of DROP can be verified by the experimental results in terms of bringing down the extra cost on resources and energy of the substrate network during the task execution for big data. © 2023 Elsevier B.V.
关键词	Big data Computation theory Deep learning Drops E-learning Network embeddings Cloud-computing Deep reinforcement learning Embeddings Federated optimization Optimisations Reinforcement learnings Research focus Task executions Task-processing Virtual network embedding
DOI	10.1016/j.future.2023.06.027
收录类别	EI ; SCIE
语种	英语
WOS研究方向	Computer Science
WOS类目	Computer Science, Theory & Methods
WOS记录号	WOS:001056442300001
出版者	Elsevier B.V.
EI入藏号	20233114474913
原始文献类型	Journal article (JA)
引用统计	被引频次[WOS]：0 [WOS记录] [WOS相关记录]
文献类型	期刊论文
条目标识符	https://ir.cqcet.edu.cn/handle/39TD4454/17955
专题	智慧健康学院人工智能与大数据学院
作者单位	Big Data and Optimization Research Institute, Chongqing College Of Electronic Engineering, Chongqing; 401331, China
第一作者单位	重庆电子科技职业大学
推荐引用方式 GB/T 7714	Xiao, Shan,Wu, Chunyi. Explore deep reinforcement learning for efficient task processing based on federated optimization in big data[J]. Future Generation Computer Systems-The International Journal of eScience,2023,149:150-161.
APA	Xiao, Shan,&Wu, Chunyi.(2023).Explore deep reinforcement learning for efficient task processing based on federated optimization in big data.Future Generation Computer Systems-The International Journal of eScience,149,150-161.
MLA	Xiao, Shan,et al."Explore deep reinforcement learning for efficient task processing based on federated optimization in big data".Future Generation Computer Systems-The International Journal of eScience 149(2023):150-161.