Hierarchical Placement Learning for Network Slice Provisioning Article Swipe
Jesutofunmi Ajayi
,
Antonio Di Maio
,
Torsten Braun
·
YOU?
·
· 2025
· Open Access
·
· DOI: https://doi.org/10.48550/arxiv.2508.06432
YOU?
·
· 2025
· Open Access
·
· DOI: https://doi.org/10.48550/arxiv.2508.06432
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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- en
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- http://arxiv.org/abs/2508.06432
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All OpenAlex metadata
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https://doi.org/10.48550/arxiv.2508.06432Digital Object Identifier
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Hierarchical Placement Learning for Network Slice ProvisioningWork title
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preprintOpenAlex work type
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2025Year of publication
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2025-08-08Full publication date if available
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Jesutofunmi Ajayi, Antonio Di Maio, Torsten BraunList of authors in order
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https://arxiv.org/abs/2508.06432Publisher landing page
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https://arxiv.org/pdf/2508.06432Direct link to full text PDF
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0Total citation count in OpenAlex
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