Design and evaluation of learning algorithms for dynamic resource management in virtual networks
Open accessAbstract
Network virtualisation is considerably gaining attention as a solution to ossification of the Internet. However, the success of network virtualisation will depend in part on how efficiently the virtual networks utilise substrate network resources. In this paper, we propose a machine learning-based approach to virtual network resource management. We propose to model the substrate network as a decentralised system and introduce a learning algorithm in each substrate node and substrate link, providing self-organization capabilities. We propose a multiagent learning algorithm that carries out the substrate network resource management in a coordinated and decentralised way. The task of these agents is to use evaluative feedback to learn an optimal policy so as to dynamically allocate network resources to virtual nodes and links. The agents ensure that while the virtual networks have the resources they need at any given time, only the required resources are reserved for this purpose. Simulations show that our dynamic approach significantly improves the virtual network acceptance ratio and the maximum number of accepted virtual network requests at any time while ensuring that virtual network quality of service requirements such as packet drop rate and virtual link delay are not affected.
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Mijumbi et al. [312] Q-learning based action selection approach for resource allocation outperforms greedy and softmax in ns-3 simulation with real Internet traffic traces.
“This Q-learning based action selection approach for resource allocation outperformsgreedy and softmax in ns-3 simulation with real Internet traffic traces.”
From A comprehensive survey on machine learning for networking: Evolution, applications and research opportunities (2018) · cites this paper as [312]
Mijumbi et al. [312] FNN prediction accuracy is approximately 90% based on real-time VoIP traffic traces on a virtualized IMS.
“Evaluation based on real-time VoIP traffic traces on a virtualized IP Multimedia Subsystem (IMS) reveals a prediction accuracy of approximately 90%.”
From A comprehensive survey on machine learning for networking: Evolution, applications and research opportunities (2018) · cites this paper as [312]
Mijumbi et al. [312] proposed method improves the ratio of accepting VNs without affecting their QoSs compared to static allocation.
“Furthermore, in comparison to static allocation, the proposed method improve the ratio of accepting VNs without affecting their QoSs.”
From A comprehensive survey on machine learning for networking: Evolution, applications and research opportunities (2018) · cites this paper as [312]
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Network Function Virtualization: State-of-the-Art and Research Challenges
Mijumbi, R., Serrat, J., Gorricho, J.-L., Bouten, N., De Turck, F., & Boutaba, R. (2016) · IEEE Communications Surveys & Tutorials · doi:10.1109/comst.2015.2477041 · Cited by 1,933 · see citing works
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- Authors
- Mijumbi, R., Gorricho, J.-L., Serrat, J., Claeys, M., De Turck, F., & Latre, S.
- Affiliations
- Universitat Politècnica de Catalunya (ES)
- Ghent University (BE)
- Venue
- 2014 IEEE Network Operations and Management Symposium (NOMS)
- Publisher
- IEEE
- Published
- 2014-05
- Pages
- 1-9
- OpenAlex
- W2040672732
- Cited by
- 109
- References
- 25
- Citations by year
- 2025: 1 · 2024: 4 · 2023: 10 · 2022: 7 · 2021: 13 · 2020: 16 · 2019: 22 · 2018: 8 · 2017: 3 · 2016: 7 · 2015: 11 · 2014: 7
- FWCI
- 7.27
- Type
- conference paper
- Language
- en
- Open access
- green
- hdl.handle.net
- Licence
- other-oa
- Locations
- IEEE/IFIP Network Operations and Management Symposium (NOMS) (publishedVersion)
- UPCommons institutional repository (Universitat Politècnica de Catalunya) (acceptedVersion, other-oa)
- RECERCAT (Consorci de Serveis Universitaris de Catalunya) (acceptedVersion, other-oa)
- Ghent University Academic Bibliography (Ghent University) (submittedVersion)
- RECERCAT (Consorci de Serveis Universitaris de Catalunya) (submittedVersion, public-domain)
- Topics
- Physical Sciences › Computer Science › Computer Networks and Communications › Software-Defined Networks and 5G
- Physical Sciences › Engineering › Electrical and Electronic Engineering › Advanced Optical Network Technologies
- Physical Sciences › Computer Science › Information Systems › Cloud Computing and Resource Management
- Keywords
- virtual network embedding, network virtualization, dynamic resource allocation, substrate network, multi-agent learning, self-organization, quality of service, packet loss rate, learning algorithms
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