Managing and orchestrating the behaviour of virtualized Network Functions (VNFs) remains a major challenge due to their heterogeneity and the ever increasing resource demands of the served flows. In this thesis, we propose a novel VNF manager (VNFM) that employs a parameterized actions-based reinforcement learning mechanism to simultaneously decide on the optimal VNF management action (e.g., migration, scaling, termination or rebooting) and the action's corresponding configuration parameters (e.g., migration location or amount of resources needed for scaling ). More precisely, we first propose a novel parameterized-action Markov decision process (PAMDP) model to accurately describe each VNF, instances of its components and their communication as well as the set of permissible management actions by the VNFM and the rewards of realizing these actions. The use of parameterized actions allows us to rigorously represent the functionalities of the VNFM in order perform various Lifecycle management (LCM) operations on the VNFs. Next, we propose a two-stage reinforcement learning (RL) scheme that alternates between learning an action-value function for the discrete LCM actions and updating the actions parameters selection policy. In contrast to existing machine learning schemes, the proposed work uniquely provides a holistic management platform the unifies individual efforts targeting individual LCM functions such as VNF placement and scaling. Performance evaluation results demonstrate the efficiency of the proposed VNFM in maintaining the required performance level of the VNF while optimizing its resource configurations.
| Identifer | oai:union.ndltd.org:uottawa.ca/oai:ruor.uottawa.ca:10393/42004 |
| Date | 15 April 2021 |
| Creators | Li, Xinrui |
| Contributors | Samaan, Nancy A., Karmouch, Ahmed |
| Publisher | Université d'Ottawa / University of Ottawa |
| Source Sets | Université d’Ottawa |
| Language | English |
| Detected Language | English |
| Type | Thesis |
| Format | application/pdf |
Page generated in 0.0017 seconds