# Customer-Oriented Quality of Service Management Method for the Future Intent-Based Networking
## 2 Related Work  
In this part, we illustrate the research status of the development of the quality of service management method based on customers' requirements for Internet service providers.  
Various works [23; 24; 25] provide a short overview of some aspects of quality of service, and identify the main issues and problems of defining and managing an SLA, based on its current standardization stage. The most recent quality of service management methods only take into account the usual priority metrics based on the type of IP service (ToS) field for making the decision about throughput allocation. Such priority-based planning methods are not optimal to provide both QoS and QoE because higher priority traffic may not require lower latency rates than lower priority traffic (e.g., traffic generated by medical sensors has higher priority than traffic from streaming devices, but the latter requires lower maximum latency). To solve the problems associated with gaps between QoS and QoE, the authors [26] proposed a new model for organizing queues for the QoS paired traffic with mixed arrival distributions in the home network to make dynamic QoS-compatible planning solutions that meet the requirements for delays of all traffic, while maintaining its criticality.  
The perspectives of QoE management for future networks and applications are discussed in papers [27; 28; 29]. After motivating QoE management, it first gives an introduction to the concept by discussing its origin, key terms, and giving an overview of the most relevant existing theoretical solutions. Finally, recent research from promising technical approaches to QoE management that operate at different layers of the network stack is explained. The authors [30] presented conclusions and views on the future of QoE management, focusing on those key factors (including collaboration, business models, and key technologies) that are necessary to ultimately transform QoE-based network and application management into a reality.  
The authors in [31] proposed a unified QoE assessment system that measures user experience for virtually all types of network services. First, this structure uses a machine learning model (random forest) to classify network services, then selects different nonlinear expressions depending on the type of service and comprehensively calculates QoE using QoS metrics, including delay, packet loss, and throughput. The results of the experiments show that the proposed method can be applied to almost all types of network traffic. In [32], [PERSON] and his colleagues presented a methodology that has been adapted to quantify the widespread QoE uncertainty due to statistical errors in measuring QoS parameters. The authors [33] proposed a heuristic, greedy, and QoE-centric resource allocation algorithm, taking into account the limitations of equity and overall user satisfaction, in order to maximize mobile operators' profits while ensuring high quality experience (QoE). The proposed algorithm can cope with heterogeneous traffic, achieving significant profits and increasing QoE compared to the most advanced QoS algorithms.  
In spite of a considerable amount of works on the quality of services management method, they mainly focus only on the technical parameters of quality of service, while today, there is a need for effective methods of taking into account the customer's opinion when managing service quality. Unlike the rather network-centric concept of \"Quality of Service\", the concept of \"Quality of Experience\" has a strongly customer-oriented view of the quality of service in communication networks as well as in the Internet services. However, the relevant research in the field of QoE has so far largely ignored the question of how to implement the differentiation of quality and provide appropriate solutions adapted to end customers.