Graph neural networks for representing multivariate resource usage: A multiplayer mobile gaming case-study

Abstract

The emergence of Multiplayer Mobile Gaming (MMG) applications is intertwined with a plethora of Quality of Service and Quality of Experience requirements. Resource usage prediction can provide valuable insights into the corresponding orchestration and management process in the form of several proactive functionalities in resource scaling, service migration, task offloading and scheduling. These processes are crucial in the Cloud and Edge environments exploited by MMG applications. Thus, producing accurate resource usage predictions concerning these types of applications is of paramount importance. To that end, we propose a resource usage representation paradigm based on Graph Neural Networks (GNNs). The novelty of this approach is based on the process of leveraging the dependencies that exist among the various types of computational resources. Furthermore, we expand upon this representation approach to develop a GNN-based Encoder-Decoder model that caters to the complexities of resource usage and can provide multi-step resource usage predictions. This model is compared against numerous well-established Encoder-Decoder and Deep Learning prediction models to assess its efficiency. Finally, the proposed model is incorporated in a proactive Horizontal Autoscaling solution that manages to outperform a standard reactive Horizontal Autoscaling approach in the context of a large-scale simulation, in terms of various performance metrics, while keeping the volume of the required computational resources to a minimum. The findings of this work showcase the importance of developing novel approaches in order to represent resource usage and the numerous benefits in the context of application performance and resource consumption that may derive from such scientific endeavors.

Keywords

• Graph neural networks
• Resource usage prediction
• Deep learning
• Mobile gaming