Name
Title | ||
|---|---|---|
Dynamic Reservation and Deep Reinforcement Learning based Autonomous Resource Management for wireless Virtual Networks |
Abstract | ||
|---|---|---|
The next generation network, 5G, is expected to provide service-oriented networks where different applications are served in isolation according to their own requirements. It is challenging to have an efficient common resource allocation mechanism for virtual networks (VNs) that have different objectives. In this work, we propose a dynamic reservation and deep reinforcement learning based autonomous virtual resource management. The infrastructure provider periodically reserves the unused resource to the VNs based on their ratio of minimum resource requirements. Then, the VNs autonomously control their resource allocation by using deep reinforcement learning based on the average quality of service utility and resource utilization of their users. With the defined design pattern in this paper, virtual operators can customize their own utility function and objective function based on their own requirements. We simulate our work to show resource utilization and satisfaction of the VNs. |
Year | DOI | Venue |
|---|---|---|
2018 | 10.1109/PCCC.2018.8710960 | 2018 IEEE 37th International Performance Computing and Communications Conference (IPCCC) |
Keywords | Field | DocType |
network slicing,network virtualization,deep reinforcement learning | Resource management,Reservation,Wireless,Computer science,Computer network,Reinforcement learning | Conference |
ISSN | ISBN | Citations |
1097-2641 | 978-1-5386-6809-2 | 0 |
PageRank | References | Authors |
0.34 | 3 | 3 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Guolin Sun | 1 | 12 | 3.84 |
| Gebrekidan Tesfay Zemuy | 2 | 0 | 0.34 |
| Kun Xiong | 3 | 9 | 1.57 |