1. Aiming in Harsh Environments: A New Framework for Flexible and Adaptive Resource Management
- Author
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Zou, Jiaqi, Liu, Rui, Wang, Chenwei, Cui, Yuanhao, Zou, Zixuan, Sun, Songlin, and Adachi, Koichi
- Subjects
Computer Science - Networking and Internet Architecture ,Computer Science - Performance - Abstract
The harsh environment imposes a unique set of challenges on networking strategies. In such circumstances, the environmental impact on network resources and long-time unattended maintenance has not been well investigated yet. To address these challenges, we propose a flexible and adaptive resource management framework that incorporates the environment awareness functionality. In particular, we propose a new network architecture and introduce the new functionalities against the traditional network components. The novelties of the proposed architecture include a deep-learning-based environment resource prediction module and a self-organized service management module. Specifically, the available network resource under various environmental conditions is predicted by using the prediction module. Then based on the prediction, an environment-oriented resource allocation method is developed to optimize the system utility. To demonstrate the effectiveness and efficiency of the proposed new functionalities, we examine the method via an experiment in a case study. Finally, we introduce several promising directions of resource management in harsh environments that can be extended from this paper., Comment: 8 pages, 4 figures, to appear in IEEE Network Magazine, 2022
- Published
- 2022