Hierarchical Placement Learning for Network Slice Provisioning

TL;DR

This paper introduces a hierarchical bandit-based approach for network slice placement in edge networks, improving request acceptance and resource efficiency through online learning.

Contribution

It presents a novel two-level hierarchical bandit algorithm for scalable, online service function chain placement in edge networks.

Findings

Achieves 5% lower resource utilization.

Admits over 25% more slice requests.

Effective on real network topologies.

Abstract

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.