Paper Info
Title
Aiming in Harsh Environments: A New Framework for Flexible and Adaptive Resource Management
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 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 article.
Year
DOI
Venue
2022
10.1109/MNET.005.2100687
IEEE Network
Keywords
DocType
Volume
environment-oriented resource allocation method,self-organized service management module,environment resource prediction module,environment awareness functionality,adaptive resource management framework,flexible resource management framework,long-time unattended maintenance
Journal
36
Issue
ISSN
Citations 
4
0890-8044
0
PageRank 
References 
Authors
0.34
10
7
Name
Order
Citations
PageRank
Jiaqi Zou101.35
Rui Liu200.34
Chenwei Wang300.34
Yuanhao Cui4201.06
Zixuan Zou500.68
Songlin Sun624.08
Koichi Adachi700.34