# Cognitive Software Defined Networking and Network Function Virtualization and Applications
## 2 Summary
### Paper 2: Virtual Network Function Embedding under Nodal Outage Using Deep Q-Learning [4]  
Here, the authors propose a solution to the Virtual Network Function (VNF) embedding problem under nodal outages using Deep Reinforcement Learning (DRL). The VNF embedding problem involves finding an optimal placement of Virtual Network Functions (VNFs) in a substrate network, subject to various constraints such as resource availability, link delay, and nodal outages.  
The paper proposes a solution for the Virtual Network Function Resource Allocation (NFV-RA) problem using Deep Q-Learning (DQL) models. The DQL models are tested under various network conditions, such as network density, nodal and core capacity, nodal outages, and Virtual Network Function (VNF) complexity. A heuristic model is introduced to improve performance. The results show that the computation time increases with the increase in network density, nodal capacity, or VNF complexity, leading to faster resource depletion. The model also shows that even with a 50% nodal outage, an SAR (Service Acceptance Ratio) of 70-90% can be achieved, although at the cost of high computation time.  
In conclusion, the authors demonstrate that DRL is a promising solution to the VNF embedding problem under nodal outages. In future work, the authors plan to study the performance of the DRL model using a different DRL algorithm.  
Paper 3: Smart Site Diversity for a High-Throughput Satellite System with Software-Defined Networking and a Virtual Network Function [5]  
This article proposes a solution for the problem of performance degradation in a high-throughput satellite system due to adverse atmospheric conditions. The proposed solution is a Software-Defined Networking (SDN) and Virtual Network Function (VNF) approach that dynamically switches transmissions among the feeder links of the system to alleviate degradation.  
The article provides a literature review of previous studies on the use of SDN and VNF in communication systems, highlighting the potential benefits of these technologies for enhancing the reliability and resilience of communication systems.  
The authors explore algorithms based on reinforcement learning to reduce packet loss and average latency. Q-Learning performed the best among all the algorithms in all three scenarios given by the author. It produced the lowest average delay and request-loss ratio, especially in the dynamic environment (Scenario-2 and Scenario-3). In contrast, SLA and HSLA take a certain amount of time to adjust their policies and find the best link. Additionally, RR performed the worst among all the algorithms as it did not consider the network conditions and provided no feedback to the learning agent.  
Overall, the article presents a novel approach for improving the reliability and resilience of HTS communications and provides insights into the potential benefits of using SDN and VNF in satellite communication systems.  
Paper 4: Proposal and Investigation of an Artificial Intelligence (AI)-Based Cloud Resource Allocation Algorithm in Network Function Virtualization Architectures [6]  
This article proposes and evaluates the use of AI-based cloud resource allocation algorithms in NFV architectures. Among the main contributions of the article are (1) the investigation of a prediction-based algorithm for resource allocation in NFV, (2) the investigation of LSTM with asymmetric loss functions for under- and over-provision scenarios in networks, and (3) a comparison with existing work in terms of performance. Results show a 40% cost advantage over prediction techniques that minimize the symmetric cost functions of the prediction error.