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Ιδρυματικό Αποθετήριο [SANDBOX]
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| URI | http://purl.tuc.gr/dl/dias/5DA0D5E6-C377-40AE-B2F4-1EE9FEB5FD47 | - |
| Αναγνωριστικό | https://doi.org/10.1109/GLOBECOM48099.2022.10001068 | - |
| Αναγνωριστικό | https://ieeexplore.ieee.org/document/10001068 | - |
| Γλώσσα | en | - |
| Μέγεθος | 6 pages | en |
| Τίτλος | Scalable end-to-end slice embedding and reconfiguration based on independent DQN agents | en |
| Δημιουργός | Doanis Pavlos | en |
| Δημιουργός | Giannakas, Theodoros, 1990- | en |
| Δημιουργός | Spyropoulos Thrasyvoulos | en |
| Δημιουργός | Σπυροπουλος Θρασυβουλος | el |
| Εκδότης | Institute of Electrical and Electronics Engineers | en |
| Περιγραφή | The research leading to these results has been supported in part by the H2020 MonB5G Project (grant agreement no. 871780) and in part by the H2020 SEMANTIC Project (grant agreement no. 861165). | en |
| Περίληψη | Network slicing in beyond 5G systems facilitates the creation of customized virtual networks/services, referred to as “slices”, on top of the physical network infrastructure. Efficient and dynamic orchestration of slices is needed to ensure the stringent and diverse service level agreements (SLAs) required by different services. In this paper, we provide a model that attempts to capture the problem of dynamic slice embedding and reconfiguration supporting a multi-domain setup and diverse, end-to-end SLAs. We then show that such problems can be optimally solved, in theory, with (tabular) Reinforcement Learning algorithms (e.g., Q-learning) even under, a priori, unknown demand dynamics for each slice. Nevertheless, the state and action complexity of such algorithms is prohibitive, even for very small scenarios. To this end, we propose a novel scheme based on independent DQN agents: The DQN component implements approximate Q-learning, based on simple, generic DNNs for value function approximation, radically reducing state space complexity; the independent agents then tackle the equally important issue of exploding action complexity arising from the combinatorial nature of embedding multiple VNFs per slice, multiple slices, over multiple domains and computing nodes therein. Using realistic data, we show that the proposed algorithm reduces convergence time by orders of magnitude with minimum penalty of decision optimality. | en |
| Τύπος | Πλήρης Δημοσίευση σε Συνέδριο | el |
| Τύπος | Conference Full Paper | en |
| Άδεια Χρήσης | http://creativecommons.org/licenses/by/4.0/ | en |
| Ημερομηνία | 2024-08-20 | - |
| Ημερομηνία Δημοσίευσης | 2022 | - |
| Θεματική Κατηγορία | VNF placement | en |
| Θεματική Κατηγορία | Network Slicing | en |
| Θεματική Κατηγορία | 5G Networks | en |
| Θεματική Κατηγορία | Reinforcement Learning | en |
| Θεματική Κατηγορία | Deep-Q Network | en |
| Βιβλιογραφική Αναφορά | P. Doanis, T. Giannakas and T. Spyropoulos, "Scalable end-to-end slice embedding and reconfiguration based on independent DQN agents," in 2022 IEEE Global Communications Conference - Proceedings (GLOBECOM 2022), Rio de Janeiro, Brazil, 2022, pp. 3429-3434, doi: 10.1109/GLOBECOM48099.2022.10001068. | en |
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