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Hierarchical Reinforcement Learning for the Dynamic VNE with Alternatives Problem
Ali Al Houss · 2026-04-29 · via cs.LG updates on arXiv.org

This paper has been withdrawn by Ali Al Housseini

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Abstract:Virtual Network Embedding (VNE) is a key enabler of network slicing, yet most formulations assume that each Virtual Network Request (VNR) has a fixed topology. Recently, VNE with Alternative topologies (VNEAP) was introduced to capture malleable VNRs, where each request can be instantiated using one of several functionally equivalent topologies that trade resources differently. While this flexibility enlarges the feasible space, it also introduces an additional decision layer, making dynamic embedding more challenging. This paper proposes HRL-VNEAP, a hierarchical reinforcement learning approach for VNEAP under dynamic arrivals. A high-level policy selects the most suitable alternative topology (or rejects the request), and a low-level policy embeds the chosen topology onto the substrate network. Experiments on realistic substrate topologies under multiple traffic loads show that naive exploitation strategies provide only modest gains, whereas HRL-VNEAP consistently achieves the best performance across all metrics. Compared to the strongest tested baselines, HRL-VNEAP improves acceptance ratio by up to \textbf{20.7\%}, total revenue by up to \textbf{36.2\%}, and revenue-over-cost by up to \textbf{22.1\%}. Finally, we benchmark against an MILP formulation on tractable instances to quantify the remaining gap to optimality and motivate future work on learning- and optimization-based VNEAP solutions.
Comments: This paper has been rejected from the conferences i submitted it, and it turns out that contains several errors, please review section of MILP
Subjects: Networking and Internet Architecture (cs.NI); Machine Learning (cs.LG); Multiagent Systems (cs.MA)
Cite as: arXiv:2512.05207 [cs.NI]
  (or arXiv:2512.05207v2 [cs.NI] for this version)
  https://doi.org/10.48550/arXiv.2512.05207

arXiv-issued DOI via DataCite

Submission history

From: Ali Al Housseini [view email]
[v1] Thu, 4 Dec 2025 19:22:40 UTC (1,831 KB)
[v2] Mon, 27 Apr 2026 21:24:18 UTC (1 KB) (withdrawn)

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