Leveraging Graph Neural Networks for SLA Violation Prediction in Cloud Computing

Angelos-Christos Maroudis, Theodoros Theodoropoulos, John Violos, Aris Leivadeas, Konstantinos Tserpes

Abstract: In this paper we examine different approaches for the prediction of Service Level Agreements (SLAs) violations that occur during the service provisioning between cloud customers and providers. Despite the fact that there are many network metrics that involve the server - client interaction, it is an open research question how these available metrics can be used by a SLA prediction mechanism. We study three different data representation models for the network characteristics, a time series, a content and a context representation. We see that a context approach using graph representations captures efficiently the associativity of clients and improves the performance of traditional SLA violation prediction models when it is combined with them. The prediction of the SLA violations takes place using neural networks, making us propose a composite SLA prediction model that leverages Graph Neural Networks (GNNs). In our research, we put special emphasis and try different variations on how we construct the graphs. We perform an extensive performance evaluation of 23 different SLA prediction models that can be grouped into the three representations categories, namely the vector models that are based on network features, sequential models that leverage the temporal evolution of QoS metrics and Graph models that take into consideration the associativity of the clients. The experimental results show that our proposed GNN-based model can significantly improve the accuracy of SLA violation prediction, making it a useful tool for Cloud and Service providers.