MDP and Machine Learning-Based Cost-Optimization of Dynamic Resource Allocation for Network Function Virtualization
Abstract
The introduction of Network Functions Virtualization (NFV) enables service providers to offer software-defined network functions with elasticity and flexibility. Its core technique, dynamic allocation procedure of NFV components onto cloud resources requires rapid response to changes on-demand to remain cost and QoS effective. In this paper, Markov Decision Process (MDP) is applied to the NP-hard problem to dynamically allocate cloud resources for NFV components. In addition, Bayesian learning method is applied to monitor the historical resource usage in order to predict future resource reliability. Experimental results show that our proposed strategy outperforms related approaches.
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Shi et al. [410] proposed method outperforms greedy methods in terms of overall cost using WorkflowSim simulator.
“Using WorkflowSim simulator, the authors demonstrate that the proposed method outperforms greedy methods in terms of overall cost.”
From A comprehensive survey on machine learning for networking: Evolution, applications and research opportunities (2018) · cites this paper as [410]
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- Authors
- Shi, R., Zhang, J., Chu, W., Bao, Q., Jin, X., Gong, C., Zhu, Q., Yu, C., & Rosenberg, S.
- Affiliations
- DELL (United States) (US)
- Carnegie Mellon University (US)
- Venue
- 2015 IEEE International Conference on Services Computing
- Publisher
- IEEE
- Published
- 2015-06
- Pages
- 65-73
- OpenAlex
- W1525264275
- Cited by
- 74
- References
- 37
- Citations by year
- 2026: 1 · 2025: 2 · 2024: 6 · 2023: 8 · 2022: 3 · 2021: 13 · 2020: 14 · 2019: 12 · 2018: 8 · 2017: 5 · 2016: 2
- FWCI
- 3.72
- Type
- conference paper
- Language
- en
- Open access
- closed
- Locations
- IEEE International Conference on Services Computing (publishedVersion)
- Topics
- Physical Sciences › Computer Science › Computer Networks and Communications › Software-Defined Networks and 5G
- Physical Sciences › Computer Science › Information Systems › Cloud Computing and Resource Management
- Physical Sciences › Engineering › Electrical and Electronic Engineering › Advanced Optical Network Technologies
- Keywords
- network function virtualization, dynamic resource allocation, cloud resource allocation, cost optimization, Markov decision process, Bayesian learning, resource usage prediction, software-defined networking, quality of service
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