Assessment of experience quality has become vital to ensure that mobile users have a satisfactory and efficient experience [12]. Combining advanced resource allocation algorithms and QoS optimization techniques is critical to maximize user QoE [13]. Understanding the relationship between QoS and QoE is important to optimize multimedia services in edge networks. While QoS focuses on objective aspects such as packet loss and latency, QoE reflects how users perceive and feel about the service. Although some models try to connect these two dimensions, many fail to capture the full complexity of the user experience. We claim that a comprehensive approach is necessary, which considers technical factors and users' subjective perceptions. In the context of smart cities, the joint and effective management of QoS and QoE strengthens networks at the edge to face the challenges of the diversity of connected objects and the complexity of urban communications [14]. In recent years, there has been a proliferation of solutions to the challenges of allocating resources in edge environments (including MEC). In this context, approaches such as auction-based optimization have shown significant improvements in system efficiency and end-user experience since, at each iteration, the auction algorithm seeks to provide the appropriate network resources to the best-equipped client to receive them [15]. Machine learning (ML) also offers a powerful approach in various fields of knowledge, experiencing remarkable growth due to its ability to automate decision-making processes and identify patterns in large volumes of data. ML has excellent potential to classify services in solutions involving edge computing and IoT [16]. Providing processing and provisioning services closer to users is essential for IoT devices, which often require low-latency responses, as well as for services that need high bandwidth availability. ML applications in this area include optimizing resource allocation and improving security by analyzing network traffic patterns. In [17], ML techniques applicable to edge computing are described, especially for efficiently managing data flows. In addition, the authors in [18] explore how ML frameworks are adapted to improve the functionality of IoT systems. In this work, we present Ubunye, a novel edge node orchestration service, with the following contributions:  
1. A service classifier based on ML techniques allows users' service requests to be identified and categorized accurately in classifying services based on high bandwidth availability or low latency requirements. 2. An allocation mechanism based on three goals: * Computational efficiency of the Ubunye auction algorithm regarding CPU and memory consumption. * Capacity to meet the user's QoE regarding latency, bandwidth, and user request success rate. * Ability to continue meeting requirements in a faulty event. The Ubunye service differentiates itself from existing proposals by integrating a service classification mechanism based on machine learning. It categorizes user requests into high-bandwidth, low-latency groups, integrated with an edge node allocation service developed in this work. The adaptability of machine learning and a focus on QoS and QoE positions Ubunye as an innovation over existing methods, ultimately leading to better service provisioning in edge computing environments. This article is organized as follows: Section 2 presents related work contextualizing Ubunye in the current edge computing orchestration services landscape. Section 3 describes the Ubunye architecture. Sections 4 and 5 present the Ubunye classifier and allocator, respectively. The experimental evaluation and results are described in Section 6. Section 7 concludes the article, highlighting the main contributions and future work.