A Model Based RL Admission Control Algorithm for Next Generation Networks
Abstract
In this paper we study the call admission control problem to optimize the network operators revenue guaranteeing quality of service to the end users. We consider a network scenario where each class of service is characterized by a different constant bit rate and an associated revenue. We formulate the problem as a semi-Markov decision process, and we use a model based reinforcement learning approach. Other traditional algorithms require an explicit knowledge of the state transition models while our solution learn it on-line. We will show how our policy provides better solution than a classic greedy algorithm.
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RL can handle MDP problems with very large state spaces and unknown state transition probabilities, and has been successfully applied to solve MDP-based admission control problems in networking.
“Therefore, RL, that can handle MDP problems with very large state spaces and unknown state transition probabilities, has been successfully applied to solve MDP-based admission control problems in networking.”
From A comprehensive survey on machine learning for networking: Evolution, applications and research opportunities (2018) · cites this paper as [311]
The admission control problem has also been formulated as an MDP.
“Besides NN and BN, the admission control problem has also been formulated as an MDP [311,458].”
From A comprehensive survey on machine learning for networking: Evolution, applications and research opportunities (2018) · cites this paper as [311]
Cites
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Robust dynamic admission control for unified cell and call QoS in statistical multiplexers
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Call admission control in cellular networks: A reinforcement learning solution
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Boutaba, R., Salahuddin, M. A., Limam, N., Ayoubi, S., Shahriar, N., Estrada-Solano, F., & Caicedo, O. M. (2018) · Journal of Internet Services and Applications · doi:10.1186/s13174-018-0087-2 · Cited by 1,036 · see citing works
- Authors
- Mignanti, S., Giorgio, A. D., & Suraci, V.
- Affiliations
- Sapienza University of Rome (IT)
- Venue
- 2009 Eighth International Conference on Networks
- Publisher
- IEEE
- Published
- 2009
- Pages
- 191-196
- OpenAlex
- W2032674405
- Cited by
- 9
- References
- 8
- Citations by year
- 2023: 2 · 2022: 1 · 2021: 1 · 2019: 1 · 2018: 1
- FWCI
- 0.81
- Type
- conference paper
- Language
- en
- Open access
- closed
- Locations
- International Conference on Networks (publishedVersion)
- IRIS eCampus Telematic University (Università degli Studi eCampus) (submittedVersion)
- IRIS Research product catalog (Sapienza University of Rome) (submittedVersion)
- Topics
- Physical Sciences › Computer Science › Computer Networks and Communications › Wireless Communication Networks Research
- Physical Sciences › Computer Science › Computer Networks and Communications › Network Traffic and Congestion Control
- Physical Sciences › Engineering › Electrical and Electronic Engineering › Advanced Wireless Network Optimization
- Keywords
- call admission control, next generation networks, model-based reinforcement learning, semi-Markov decision process, quality of service, class of service, revenue optimization, greedy algorithm comparison
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