Efficient Network Slicing with Dynamic Resource Allocation

doi:10.12142/ZTECOM.202101003

ZTE Communications ›› 2021, Vol. 19 ›› Issue (1): 11-19.DOI: 10.12142/ZTECOM.202101003

收稿日期:2020-12-09 出版日期:2021-03-25 发布日期:2021-04-09

Efficient Network Slicing with Dynamic Resource Allocation

JI Hong¹(), ZHANG Tianxiang², ZHANG Kai¹, WANG Wanyuan¹, WU Weiwei¹

^1.School of Computer Science and Engineering, Southeast University, Nanjing 211189, China
^2.ZTE corporation, Shenzhen 518057, China

Received:2020-12-09 Online:2021-03-25 Published:2021-04-09
About author:JI Hong (hong_ji@seu.edu.cn) received the B.S. degree from School of Economics and Management from Xidian University, China in 2018. He is currently a master student at School of Cyber Science and Engineering, Southeast University, China. His research interests lie in network optimization and intelligent decision making.|ZHANG Tianxiang received the B.S. degree from Nanjing University of Aeronautics and Astronautics, China. He is currently an engineer with ZTE Corporation. His research interests include traffic scheduling and graph neural networks.|ZHANG Kai is currently a master student at School of Cyber Science and Engineering, Southeast University, China. His research interests include graph neural networks and resource allocation and reinforcement learning.|WANG Wanyuan is an assistant professor with the School of Computer Science and Engineering, Southeast University, China. He received his Ph. D. degree in computer science from Southeast University in 2016. He has published several articles in refereed journals and conference proceedings, such as the IEEE Transactions on Mobile Computing, IEEE Journal on Selected Areas in Communications, IEEE Transactions on Cybernetics, AAAI, and AAMAS. He won the best student paper award from ICTAI14. His main research interests include artificial intelligence, multiagent systems, and game theory.|WU Weiwei is a professor in the School of Computer Science and Engineering, Southeast University, China. He received his B.Sc. degree fron South China University of Technology，China and the Ph.D. degree from City University of Hong Kong (CityU)，China and University of Science and Technology of China (USTC) in 2011, and went to Nanyang Technological University, Singapore for post-doctorial research in 2012. He has published over 50 peer-reviewed papers in international conferences/journals, and serves as TPCs and reviewers for several top international journals and conferences. His research interests include optimizations and algorithm analysis, wireless communications, crowdsourcing, cloud computing, reinforcement learning, game theory and network economics.

摘要/Abstract

Abstract:

With the rapid development of wireless network technologies and the growing demand for a high quality of service (QoS), the effective management of network resources has attracted a lot of attention. For example, in a practical scenario, when a network shock occurs, a batch of affected flows needs to be rerouted to respond to the network shock to bring the entire network deployment back to the optimal state, and in the process of rerouting a batch of flows, the entire response time needs to be as short as possible. Specifically, we reduce the time consumed for routing by slicing, but the routing success rate after slicing is reduced compared with the unsliced case. In this context, we propose a two-stage dynamic network resource allocation framework that first makes decisions on the slices to which flows are assigned, and coordinates resources among slices to ensure a comparable routing success rate as in the unsliced case, while taking advantage of the time efficiency gains from slicing.

Key words: network slicing, dynamic resource allocation, reinforcement learning

. [J]. ZTE Communications, 2021, 19(1): 11-19.

JI Hong, ZHANG Tianxiang, ZHANG Kai, WANG Wanyuan, WU Weiwei. Efficient Network Slicing with Dynamic Resource Allocation[J]. ZTE Communications, 2021, 19(1): 11-19.

图/表 13

Table 1 Notation overview

Notation	Description
$V = {v 1, . . . ?, v n}$	the set of nodes
$E = {v s, ? v d i \| i = 1,2, . . . ?, m}$	the set of edges
$e i$ or $e s d$	a certain link
$A = {a 1, . . . ?, a m}$	available bandwidth per link
$G = {V, ? E, ? A}$	the set of a network
$F = {v s, ? v d j \| j = 1, . . . ?, h}$	the set of flows
$f j$ or $f s d$	a certain flow
$d j$	demand of a flow
$P j$	a path the flow $j$ takes
$S = {s 1, . . . ?, s k, . . . ?, s K}$	set of slices
$f j k ∈ F k$	set of flows to be deployed in slice $k$

Table 1 Notation overview

Notation	Description
$V = {v 1, . . . ?, v n}$	the set of nodes
$E = {v s, ? v d i \| i = 1,2, . . . ?, m}$	the set of edges
$e i$ or $e s d$	a certain link
$A = {a 1, . . . ?, a m}$	available bandwidth per link
$G = {V, ? E, ? A}$	the set of a network
$F = {v s, ? v d j \| j = 1, . . . ?, h}$	the set of flows
$f j$ or $f s d$	a certain flow
$d j$	demand of a flow
$P j$	a path the flow $j$ takes
$S = {s 1, . . . ?, s k, . . . ?, s K}$	set of slices
$f j k ∈ F k$	set of flows to be deployed in slice $k$

Figure 1 A network with 3 nodes and 3 edges

Figure 2 Network slices in our slicing model

Figure 3 Interaction between policy and environment

Figure 4 Reinforcement learning (RL) training architecture

Figure 5 Dynamic resource adjustment

Figure 6 An example of network topology of the ring network

Figure 7 Loss during training is based on a 2080 Ti graphics processing unit (GPU) and 120 sampling processes (2 Xeon CPUs)

Figure 8 Total reward of different methods

Figure 9 Success Rate of different methods

Figure 10 Relative failure number of dynamic resource adaptation vs. static slicing

Figure 11 Comparison of calculation time of different methods

Figure 12 Comparison of the effectiveness of combined methods

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Efficient Network Slicing with Dynamic Resource Allocation

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