التفاصيل البيبلوغرافية
| العنوان: |
Explore deep reinforcement learning for efficient task processing based on federated optimization in big data. |
| المؤلفون: |
Xiao, Shan1 (AUTHOR) frodoxiao@163.com, Wu, Chunyi1 (AUTHOR) wucy1851998@163.com |
| المصدر: |
Future Generation Computer Systems. Dec2023, Vol. 149, p150-161. 12p. |
| مصطلحات موضوعية: |
*BIG data, *POWER resources, DEEP reinforcement learning, REINFORCEMENT learning, VIRTUAL networks, DEEP learning |
| مستخلص: |
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. • Based on the idea of multi-field integration, as well as the rapid growth of user demand led to the surge of tasks, the issue of large-scale task collaborative processing in big data is put forward. • The critical section of task processing problem in cloud computing is formalized by using deep reinforcement learning. The deep reinforcement learning model is trained to deduce the better decision-making on the virtual link embedding strategy. • The virtual node mapping is achieved by designed deep feature learning network to realize the kind of two-phase optimization for task allocation in the introduced federated learning structure. • The superiority of DROP for large-scale tasks co-processing is evaluated in comparison with state-of-the-art methods in the distributed federated learning environment. [ABSTRACT FROM AUTHOR] |
|
Copyright of Future Generation Computer Systems is the property of Elsevier B.V. and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.) |
| قاعدة البيانات: |
Business Source Index |
