# Intelligent Network Service Optimization in the Context of 5G/NFV
## 1 Introduction
At its core, the MAPE model iterates the following steps: introducing mechanisms to monitor a target system and its environmental variables, considering observations in tandem with the system's architectural model, assessing improvement and optimization possibilities, intuitively deciding on a course of action, and ultimately deploying any possible selected alterations. It was primarily conceptualized by IBM in 2006 as a reference architecture, and its implementation was achieved mostly by the open-source solutions the company was supporting during this time [16]. In the modern era, the MAPE concept was furthered by the Sonata MANO platform [17] as it was gradually being incorporated in the 5G ecosystem. Since then, conditions have become more favourable for elaborating MAPE in CNA-related projects, as described primarily in Section 3, and its principles can easily be embodied in many use cases to overcome existing architectural limitations [18]. From this perspective, it is obvious that guaranteeing a specific QoS level is not an easy task, as the architecture of modern NSs has become more complex, and at the same time, the users' requirements have increased. Even though technologies and frameworks for the management and orchestration of the physical and virtual resources are available, the analysis of each NS performance based on the users' requirements and their life-cycle management in real time is not guaranteed. In this context, we study an intelligent MAPE-based optimization scheme that utilizes machine learning (ML) techniques. The performances of NSs, as well as the availability of the resources of the physical and virtual infrastructure, are analysed by trained ML models, in order to quickly and aptly adapt the orchestration actions of CNI resource allocation and scaling in highly dynamic and unknown critical environments. Our study confirms that the inclusion of quality-of-experience (QoE) metrics as input to the applied ML algorithms contributes greatly to the optimization of video streaming quality. Our proposal for an intelligent optimization service can be also viewed from a more generic perspective and applied in other use cases where cyber-physical systems are deployed to automate and optimize operations, such as those in the smart manufacturing sector and new-generation warehouse and logistics systems [19, 20]. In these use cases, a digital twin of the processes in action are at the focus of the activities of the international research community and of IT solution designers. Our approach can be applied and tailored to these use cases to enhance current approaches with respect to intelligence and security. The synchronization between cyber systems and physical entities (as also in the case presented in this paper) is a crucial parameter for achieving the overall goal of action optimization in each case. Our experimental results show that the fluctuations in QoE metric values provide the advantage of swifter and more optimal service recuperation while handling parameters easily susceptible to change such as frame rate, bit rate, and packet loss. The remainder of this paper is organized as follows. In Section 2, we present related work, while in Section 3, the proposed MAPE-based architecture is illustrated. The adopted methodology of MAPE's analysis service is documented in Section 4. Finally, Sections 5 and 6, respectively, provide a concise description of the experimental results and conclude the paper with a presentation of future work.