Research Article
Dynamic Communication and Computation Resource Allocation Algorithm for End-to-End Slicing in Mobile Networks
@INPROCEEDINGS{10.1007/978-3-030-90196-7_22, author={Hong Xu and Zhou Tong and Hong Shen and Tiankui Zhang}, title={Dynamic Communication and Computation Resource Allocation Algorithm for End-to-End Slicing in Mobile Networks}, proceedings={Artificial Intelligence for Communications and Networks. Third EAI International Conference, AICON 2021, Xining, China, October 23--24, 2021, Proceedings, Part I}, proceedings_a={AICON}, year={2021}, month={11}, keywords={Network slicing Resource allocation Dynamic}, doi={10.1007/978-3-030-90196-7_22} }- Hong Xu
Zhou Tong
Hong Shen
Tiankui Zhang
Year: 2021
Dynamic Communication and Computation Resource Allocation Algorithm for End-to-End Slicing in Mobile Networks
AICON
Springer
DOI: 10.1007/978-3-030-90196-7_22
Abstract
In the mobile network, to support business diversity and meet the differentiated needs of vertical industries, network slicing technology has emerged. Network slicing is required in both the core network and the radio access network, that is, to achieve end-to-end network slicing. Most of the current network slicing service requirements are dynamic, in end-to-end network slicing, a deep Q network (DQN)-based two-stage joint allocation algorithm for communication and computation resource is proposed to solve the problem of dynamic changes of network slicing data queues, radio channel status, and physical network topology. The dynamic resource allocation model of end-to-end slicing is constructed, and the dynamic joint allocation of communication and computation resource is carried out to maximize the overall utility of the network on a long-term scale. The dynamic migration of virtualization network function (VNF) and the flexible allocation of virtual network resources are realized according to the service state and quality of service (QoS) requirements of virtual network users. The simulation results show that the proposed algorithm can optimize the overall utility of the network on a long-term scale, improve the long-term average revenue, and reduce the average cost of the system.
