Hierarchical Placement Learning for Network Slice Provisioning
By: Jesutofunmi Ajayi, Antonio Di Maio, Torsten Braun
Potential Business Impact:
Makes phone networks faster and use less power.
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.
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