  Deployment, operation and maintenance of large IT systems becomes
increasingly complex and puts human experts under extreme stress when problems
occur. Therefore, utilization of machine learning (ML) and artificial
intelligence (AI) is applied on IT system operation and maintenance -
summarized in the term AIOps. One specific direction aims at the recognition of
re-occurring anomaly types to enable remediation automation. However, due to IT
system specific properties, especially their frequent changes (e.g. software
updates, reconfiguration or hardware modernization), recognition of reoccurring
anomaly types is challenging. Current methods mainly assume a static
dimensionality of provided data. We propose a method that is invariant to
dimensionality changes of given data. Resource metric data such as CPU
utilization, allocated memory and others are modelled as multivariate time
series. The extraction of temporal and spatial features together with the
subsequent anomaly classification is realized by utilizing TELESTO, our novel
graph convolutional neural network (GCNN) architecture. The experimental
evaluation is conducted in a real-world cloud testbed deployment that is
hosting two applications. Classification results of injected anomalies on a
cassandra database node show that TELESTO outperforms the alternative GCNNs and
achieves an overall classification accuracy of 85.1%. Classification results
for the other nodes show accuracy values between 85% and 60%.
