  Ensuring the reliability of cloud systems is critical for both cloud vendors
and customers. Cloud systems often rely on virtualization techniques to create
instances of hardware resources, such as virtual machines. However,
virtualization hinders the observability of cloud systems, making it
challenging to diagnose platform-level issues. To improve system observability,
we propose to infer functional clusters of instances, i.e., groups of instances
having similar functionalities. We first conduct a pilot study on a large-scale
cloud system, i.e., Huawei Cloud, demonstrating that instances having similar
functionalities share similar communication and resource usage patterns.
Motivated by these findings, we formulate the identification of functional
clusters as a clustering problem and propose a non-intrusive solution called
Prism. Prism adopts a coarse-to-fine clustering strategy. It first partitions
instances into coarse-grained chunks based on communication patterns. Within
each chunk, Prism further groups instances with similar resource usage patterns
to produce fine-grained functional clusters. Such a design reduces noises in
the data and allows Prism to process massive instances efficiently. We evaluate
Prism on two datasets collected from the real-world production environment of
Huawei Cloud. Our experiments show that Prism achieves a v-measure of ~0.95,
surpassing existing state-of-the-art solutions. Additionally, we illustrate the
integration of Prism within monitoring systems for enhanced cloud reliability
through two real-world use cases.
