An Online Multi-dimensional Knapsack Approach for Slice Admission Control

2025-08-08

AI Summary

This paper addresses the online admission control problem under network slicing, where resource demands are uncertain, aiming to maximize the long-term revenue of infrastructure providers. We formulate it as an online multi-dimensional knapsack problem—the first such rigorous mapping between network slice admission and this classical online optimization framework—and propose two reservation-based online policies. These policies provide provable competitive ratio guarantees and maintain robust revenue gains even under significant heterogeneity in tenant willingness-to-pay. Monte Carlo simulations demonstrate that our approach increases revenue by up to 12.9% and reduces average resource consumption by 1.7% compared to conventional first-come-first-served admission. The core contributions are: (i) establishing the first formal equivalence between network slicing admission control and the online multi-dimensional knapsack problem; and (ii) designing a reservation mechanism with theoretical performance guarantees.

Technology Category

• Machine Learning: Online Learning & Bandits
• Planning, Routing, and Scheduling: Scheduling under Uncertainty
• Search and Optimization: Mixed Discrete/Continuous Search

Application Category

• Economics, Online Markets and Human Computation: Incentives in network design for Web infrastructures and ecosystems
• Systems and Infrastructure for Web, Mobile and WoT: Virtualization and resource management in Web systems and infrastructures
• Responsible Web: Human-perceived consequences of algorithmic deployment on the web

Abstract

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.

Problem

• Admission control for Network Slice Requests in online settings
• Maximize long-term revenue from admitted slice requests
• Optimize resource allocation among existing and new slices

Innovation

• Online Multidimensional Knapsack modeling approach
• Reservation-based policies for admission control
• Monte Carlo simulations for performance evaluation