{"type":"ScholarlyArticle","title":"FlagVNE: A Flexible and Generalizable Reinforcement Learning Framework for Network Resource Allocation","authors":[{"name":"Tianfu Wang","slug":"tianfu-wang","given":"Tianfu","family":"Wang","affiliation":"School of Computer Science and Technology , University of Science and Technology of China"},{"name":"Qilin Fan","slug":"qilin-fan","given":"Qilin","family":"Fan","affiliation":"School of Big Data and Software Engineering , Chongqing University"},{"name":"Chao Wang","slug":"chao-wang","given":"Chao","family":"Wang","affiliation":"Guangzhou HKUST Fok Ying Tung Research Institute; School of Computer Science and Technology , University of Science and Technology of China"},{"name":"Long Yang","slug":"long-yang","given":"Long","family":"Yang","affiliation":"School of Artificial Intelligence , Peking University"},{"name":"Leilei Ding","slug":"leilei-ding","given":"Leilei","family":"Ding","affiliation":"School of Computer Science and Technology , University of Science and Technology of China"},{"name":"Nicholas Jing Yuan","slug":"nicholas-jing-yuan","given":"Nicholas Jing","family":"Yuan","affiliation":""},{"name":"Hui Xiong","slug":"hui-xiong","given":"Hui","family":"Xiong","affiliation":"Thrust of Artificial Intelligence , The Hong Kong University of Science and Technology (Guangzhou )"}],"abstract":"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 https://github.com/GeminiLight/flag-vne.","doi":"10.24963/ijcai.2024/266","url":"https://ijcai.org/proceedings/2024/266","pdf":"https://ijcai.org/proceedings/2024/0266.pdf","markdown":"https://ijcai.org/proceedings/2024/266.md","bibtex":"https://ijcai.org/proceedings/2024/bibtex/266","volume":{"slug":"2024","title":"Proceedings of the Thirty-Third International Joint Conference on Artificial Intelligence","short_title":"IJCAI 2024","url":"https://ijcai.org/proceedings/2024/","year":2024,"location":"Jeju, Korea","dates":"3-9 August 2024","editors":"Kate Larson","isbn":"978-1-956792-04-1","publisher":"International Joint Conferences on Artificial Intelligence Organization","doi_prefix":"10.24963/ijcai.2024"},"section":"Data Mining","track":"Main Track","number":266,"pages":{"first":2406,"last":2414},"keywords":["Data Mining: DM: Parallel, distributed and cloud-based high performance mining","Machine Learning: ML: Applications","Data Mining: DM: Applications"],"videos":[],"references":[{"position":1,"raw":"[Blenk et al., 2018] Andreas Blenk, Patrick Kalmbach, Jo- hannes Zerwas, Michael Jarschel, Stefan Schmid, and Wolfgang Kellerer. 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