Ubunye: An MEC Orchestration Service Based on QoE, QoS, and Service Classification Using Machine Learning  
[PERSON]  
1 Departamento de Engenharia de Teleinformatica, Universidade Federal do Ceara (UFC), Campus do Pici, Bloco 725, Fortaleza 60455-970, Ceara, Brazil; [EMAIL_ADDRESS] (G.A.A.A. ); [EMAIL_ADDRESS] (A.R.R.) [PERSON]  
2 Centro de Ciencias Tecnologicas, Universidade de Fortaleza (UNIROR), Av. [PERSON], 1321, [PERSON], Fortaleza 60811-905, Ceara, Brazil; [EMAIL_ADDRESS]  
[PERSON]  
1 Departamento de Engenharia de Teleinformatica, Universidade Federal do Ceara (UFC), Campus do Pici, Bloco 725, Fortaleza 60455-970, Ceara, Brazil; [EMAIL_ADDRESS] (G.A.A.A. ); [EMAIL_ADDRESS] (A.R.R.) [PERSON]  
3 Instituto de Computacao, Universidade Federal Fluminense (UFF), Av. Gal. [PERSON], Slo Domingos, Niteroi 24210-346, Rio de Janeiro, Brazil; [EMAIL_ADDRESS] (C.R.R.) (C.R.) (C.  
space, the gap between information-rich and information-poor areas, and the scarcity of network resources among online users [2]. These disparities challenge the scientific community, governments, and the private sector to research and develop solutions to mitigate or eliminate this problem, especially with an ever-increasing demand for connectivity from applications, for example, based on the Internet of things (IoT) [3]. There is a fundamental need to rethink the projects that provide network resources, focusing on the user experience and strategies to optimize local computing resources, such as edge computing [4]. Edge computing takes advantage of distributed computing resources across the entire continuum of available network elements (edge nodes), from data production devices to the cloud. Edge computing also offers a promising concept for transferring a significant part of the cloud service to the network edge [5]. Thus, edge computing is becoming a key technology for addressing increasing latency and bandwidth challenges in modern networks, particularly MEC (multi-access edge computing) in the context of IoT applications [6]. The significance of MEC lies in its ability to bring data processing and storage closer to end mobile devices, effectively shifting a substantial portion of cloud services to the network's edge. This approach significantly reduces latency and improves network efficiency [7]. MEC optimizes resource usage and supports time-sensitive applications such as online gaming, augmented reality, and high-definition video streaming [8]. In response to the growing demand for low-latency, high-bandwidth services in mobile networks, the European Telecommunications Standards Institute (ETSI) has defined the MEC reference architecture. This architecture serves as a guide for implementing MEC systems, ensuring interoperability and cohesion between different vendors and operators [9]. However, in many MEC environments, resources are limited and may not be sufficient to meet the computing requirements of all end devices. As a result, this limitation can affect the quality of service (QoS) or the quality of experience (QoE) of users. QoS measures how well a network delivers consistent and predictable performance levels for different applications and users. Ensuring QoS is essential in networks that handle multiple types of traffic. On the other hand, QoE defines how users perceive and interact with a service, including aspects such as the perceived quality of streaming video, the clarity of a VoIP call, and the responsiveness of a web application. QoE is crucial for services where end-user satisfaction plays a key role in determining success [10]. QoS and QoE are strongly interrelated as they approach quality of service from different perspectives. Although QoS provides the technical basis necessary for the efficient delivery of network services, QoE determines the ultimate success of these services by reflecting user satisfaction and perception. Both are essential to ensure a high-quality service experience, especially in environments where user satisfaction is critical [11].