FlagVNE: A Flexible and Generalizable Reinforcement Learning Framework for Network Resource Allocation
arxiv(2024)
摘要
Virtual network embedding (VNE) is an essential resource allocation task in
network virtualization, aiming to map virtual network requests (VNRs) onto
physical infrastructure. Reinforcement learning (RL) has recently emerged as a
promising solution to this problem. However, existing RL-based VNE methods are
limited by the unidirectional action design and one-size-fits-all training
strategy, resulting in restricted searchability and generalizability. In this
paper, we propose a FLexible And Generalizable RL framework for VNE, named
FlagVNE. Specifically, we design a bidirectional action-based Markov decision
process model that enables the joint selection of virtual and physical nodes,
thus improving the exploration flexibility of solution space. To tackle the
expansive and dynamic action space, we design a hierarchical decoder to
generate adaptive action probability distributions and ensure high training
efficiency. Furthermore, to overcome the generalization issue for varying VNR
sizes, we propose a meta-RL-based training method with a curriculum scheduling
strategy, facilitating specialized policy training for each VNR size. Finally,
extensive experimental results show the effectiveness of FlagVNE across
multiple key metrics. Our code is available at GitHub
(https://github.com/GeminiLight/flag-vne).
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