Ubunye: An MEC Orchestration Service Based on QoE, QoS, and Service Classification Using Machine Learning

The increasing adoption of Internet of Things devices has led to a significant demand for cloud services, where latency and bandwidth play a crucial role in shaping users’ perception of network service quality. However, the use of cloud services with the desired quality is not always available to all users. Furthermore, uneven network coverage in urban and rural areas has created “digital deserts”, which are characterized by a lack of connectivity resources, complicating access to cloud services. In this scenario, edge computing emerges as a promising alternative for service provision. Edge computing leverages data processing at or near the source where it is generated rather than sending it to the cloud for processing. It can lead to several advantages, such as reduced latency and lower bandwidth usage. This paper addresses the need to ensure consistent quality of experience (QoE) and quality of service (QoS) in dynamic network environments, particularly in remote regions with limited infrastructure. We propose an orchestration service called Ubunye, which operates at the network edge and selects the most appropriate edge node to fulfill a given application request while satisfying its quality requirements. Ubunye considers factors such as latency and available bandwidth when selecting a node to execute the requested service. It implements a service classification system based on machine learning (ML) techniques. The ideal edge node is chosen through a multi-faceted evaluation, which includes current CPU load, memory availability, and other relevant parameters. Experiment results show that Ubunye effectively orchestrates resources at the network edge, enhancing QoE and QoS for services that demand low latency and high bandwidth. Additionally, it showcases the ability to classify services and allocate resources under challenging network conditions.