Name
Abstract | ||
|---|---|---|
In this paper, we propose strategies and develop solutions for a network service provider (NSP) to cost-effectively provision and manage a large number of network slices. Specifically, we propose a novel framework, namely, network slice bundling, in which (1) an NSP can allocate resources and create multiple network slice bundles in advance, (2) a network slice request can be quickly instantiated in the bundle that supports its service requirements, and (3) network slices in the same bundle can share the resources and achieve a multiplexing gain by leveraging the stochastic behaviors of resource usage. Within this framework, we focus on a core problem, which is how to leverage the multiplexing gain to maximize the utility by optimally assigning multiple network slices to a set of pre-defined bundles. We formulate an optimization problem and theoretically analyze the irregularity of constraints and the difficulty of the problem. We develop a novel reinforcement learning (RL) based slice assignment solution. Finally, we conduct extensive data-driven simulation experiments. The numerical results confirm that the proposed solution can efficiently solve the network slice assignment problem and achieve significantly higher utility than the best baseline algorithm. |
Year | DOI | Venue |
|---|---|---|
2021 | 10.1109/TNSE.2020.3031347 | IEEE Transactions on Network Science and Engineering |
Keywords | DocType | Volume |
Multiplexing gain,network slice bundles,quality-of-service,reinforcement learning | Journal | 8 |
Issue | ISSN | Citations |
1 | 2327-4697 | 0 |
PageRank | References | Authors |
0.34 | 0 | 6 |