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.

• Publication year

  2025

• Venue

  IEEE Conference on Local Computer Networks

• Publication date

  2025-08-08

• Fields of study

  Computer Science, Engineering

• Identifiers
  DOI 10.48550/arXiv.2508.06432arXiv 2508.06432
• External record

  Open on Semantic Scholar

• Source metadata

  Semantic Scholar

• No claims are published for this paper.

• No concepts are published for this paper.

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