Jesutofunmi Ajayi | Searcharxiv

arXiv2025

Hierarchical Placement Learning for Network Slice Provisioning

In this work, we aim to address the challenge of slice provisioning in edge-based mobile networks. We propose a solution that learns a service function chain placement policy for Network Slice Requests, to maximize the request acceptance rate, while minimizing the average node resource utilization. To do this, we consider a Hierarchical Multi-Armed Bandit problem and propose a two-level hierarchical bandit solution which aims to learn a scalable placement policy that optimizes the stated objectives in an online manner. Simulations on two real network topologies show that our proposed approach achieves 5% average node resource utilization while admitting over 25% more slice requests in certain scenarios, compared to baseline methods.

cs.NI↗

arXiv2025

An Online Multi-dimensional Knapsack Approach for Slice Admission Control

Network Slicing has emerged as a powerful technique to enable cost-effective, multi-tenant communications and services over a shared physical mobile network infrastructure. One major challenge of service provisioning in slice-enabled networks is the uncertainty in the demand for the limited network resources that must be shared among existing slices and potentially new Network Slice Requests. In this paper, we consider admission control of Network Slice Requests in an online setting, with the goal of maximizing the long-term revenue received from admitted requests. We model the Slice Admission Control problem as an Online Multidimensional Knapsack Problem and present two reservation-based policies and their algorithms, which have a competitive performance for Online Multidimensional Knapsack Problems. Through Monte Carlo simulations, we evaluate the performance of our online admission control method in terms of average revenue gained by the Infrastructure Provider, system resource utilization, and the ratio of accepted slice requests. We compare our approach with those of the online First Come First Serve greedy policy. The simulation's results prove that our proposed online policies increase revenues for Infrastructure Providers by up to 12.9 % while reducing the average resource consumption by up to 1.7% In particular, when the tenants' economic inequality increases, an Infrastructure Provider who adopts our proposed online admission policies gains higher revenues compared to an Infrastructure Provider who adopts First Come First Serve.

cs.NI↗