Workload prediction for volatile nodes in multi-access edge networks

Advancement of edge and far-edge computing, driven by the increasing demand for real-time, data-intensive applications, has heightened the need for reliable and efficient resource management in volatile environments. This paper introduces GTMixer, a deep learning architecture tailored for predicting resource usage in volatile edge computing scenarios. GTMixer utilizes a Dynamic Temporal Graph (DTG) to capture the evolving interdependencies in workload exchanges across edge and far-edge nodes. By processing snapshots of this graph, GTMixer identifies patterns in resource utilization, even in the absence of historical CPU data. Our contributions include: (1) the creation of a DTG that reflects migration patterns and resource utilization among nodes; (2) the development of the GTMixer model, which integrates feature mixing with graph neural networks for improved predictive accuracy; and (3) empirically evaluating GTMixer against state-of-the-art models using a modified version of the Alibaba 2021 traces dataset that accounts for volatility. Our results demonstrate that GTMixer not only effectively anticipates resource requirements in unpredictable Multi-access Edge Computing (MEC) scenarios but also significantly outperforms current state-of-the-art models in terms of efficiency, showcasing its potential to enhance the reliability and performance of edge computing systems crucial for nextgeneration network technologies and applications.