Document Type
Article
Abstract
Efficient provisioning of 5G network slices is a major challenge for 5G network slicing technology. Previous slice provisioning methods have only considered network resource attributes and ignored network topology attributes. These methods may result in a decrease in the slice acceptance ratio and the slice provisioning revenue. To address these issues, we propose a two-stage heuristic slice provisioning algorithm, called RT-CSP, for the 5G core network by jointly considering network resource attributes and topology attributes in this paper. The first stage of our method is called the slice node provisioning stage, in which we propose an approach to scoring and ranking nodes using network resource attributes (i.e., CPU capacity and bandwidth) and topology attributes (i.e., degree centrality and closeness centrality). Slice nodes are then provisioned according to the node ranking results. In the second stage, called the slice link provisioning stage, the k-shortest path algorithm is implemented to provision slice links. To further improve the performance of RT-CSP, we propose RT-CSP+, which uses our designed strategy, called minMaxBWUtilHops, to select the best physical path to host the slice link. The strategy minimizes the product of the maximum link bandwidth utilization of the candidate physical path and the number of hops in it to avoid creating bottlenecks in the physical path and reduce the bandwidth cost. Using extensive simulations, we compared our results with those of the state-of-the-art algorithms. The experimental results show that our algorithms increase slice acceptance ratio and improve the provisioning revenue-to-cost ratio.
Publication Date
10-1-2019
Publication Title
Applied Sciences (Switzerland)
Volume
9
Issue
20
DOI
10.3390/app9204361
Recommended Citation
Li, Xin; Guo, Chengcheng; Xu, Jun; Gupta, Lav; and Jain, Raj, "Towards efficiently provisioning 5G core network slice based on resource and topology attributes" (2019). Computer Science Faculty Works. 21.
DOI: https://doi.org/10.3390/app9204361
Available at:
https://irl.umsl.edu/cmpsci-faculty/21