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The increasing complexity of distributed computing environments necessitates efficient resource management strategies to optimize performance and minimize resource consumption. Although proactive horizontal autoscaling dynamically adjusts computational resources based on workload predictions, existing approaches primarily focus on improving workload resource consumption, often neglecting the overhead introduced by the autoscaling system itself. This could have dire ramifications on resource efficiency, since many prior solutions rely on multiple forecasting models per compute node or group of pods, leading to significant resource consumption associated with the autoscaling system. To address this, we propose GraphOpticon, a novel proactive horizontal autoscaling framework that leverages a singular global forecasting model based on Spatiotemporal Graph Neural Networks. The experimental results demonstrate that GraphOpticon is capable of providing improved service performance, and resource consumption (caused by the workloads involved and the autoscaling system itself). As a matter of fact, GraphOpticon manages to consistently outperform other contemporary horizontal autoscaling solutions, such as Kubernetes’ Horizontal Pod Autoscaler, with improvements of 6.62% in median execution time, 7.62% in tail latency, and 6.77% in resource consumption, among others.