# Intelligent Network Service Optimization in the Context of 5G/NFV
## 3 MAPE Framework Architecture
### Analysis and Optimization Services  
The overall architectural loop of control for dynamic service self-adaptation receives input from the MS subsystem, collects monitoring metrics and preprocesses data, analyses information based on specific ML algorithms, makes predictions towards service optimization, applies actions by orchestrating resources, and executes service reconfiguration. The reference control model for resource optimization in the proposed solution is shown in Figure 2.  
The analysis service (AS) component should consider metrics received from the MS, which when combined, become as informative as possible regarding the system's current state or future trends. For this purpose, the origin of incoming monitoring metrics should be diverse and inclusive, invoking infrastructure, application-level, and QoE metrics. During the implementation phase, we considered the direction of the type of learning that should be applied, thus realizing the optimization engine (OE). Common approaches include supervised learning (SL), unsupervised learning (UL), and reinforcement learning (RL) [34, 35]. Regarding the first case, the provided input is a labelled dataset, from which the model can learn. SL basically copes with two types of problems: classification problems where a discrete value should be predicted and regression problems in the case of continuous data. As far as UL is concerned, the provided input is not labelled, and it might not be complete or clean. UL is a type of self-organized learning that assists in finding unknown patterns in the data without existing labels. Utilizing the latter, a predefined input might or might not exist. RL is a type of learning that is based on the interaction of an agent with the  
Figure 2: Continuous optimization process. environment. An action is executed in an environment, and the feedback received assists the learning of the agent, imitating a trial-and-error approach. Of these types of learning, SL and RL are considered more appropriate for our case. In both of these cases, a proper dataset should be created. Throughout the implementation phase, versions of SL might include deep learning (DL) [36], as well as streaming linear algorithms implemented in Apache Spark [37]. The goals of these SL approaches will be to detect network anomalies and congestion, overutilization of resources, or underutilization of the overall system. On the other hand, RL will deal with the state modelling of the system, inherently focusing on all or most of its aspects, given that the collection of exploited monitoring metrics is diverse and describes the underlying system as precisely as possible. In our study, the AS part will include optimizations related to streaming video content (see Section 4.5). Quality of experience (QoE) is a widely used metric that expresses the satisfaction that a user receives by interacting with an application or a service. It is similar to the user experience (UX) concept, which originates from the telecommunications field. QoE is defined by the International Telecommunication Union (ITU) as \"the degree of delight or annoyance of the user of an application or service. It results from the fulfilment of his/her expectations with respect to the utility and/or enjoyment of the application or service in the light of the user's personality and current state\". Since the quality of the transmitted video is one of the main targets of the proposed solution, the integration of an objective or subjective QoE model aiming at calculating the mean opinion score (MOS) value is considered necessary. The MOS computation contributes to the enhancement of the OE's operation, ensuring that trustworthy decisions are made regarding the corrective actions necessary for the improvement of the deployed services. The MOS measurement, employed in the QoE and telecommunications engineering domains, expresses the overall quality of a stimulus or system. It is a commonly used measure for audiovisual quality evaluation.