一句话：提出非侵入式聚类方法 Prism，依据通信与资源使用模式从云系统海量实例中推断功能簇，提升可观测性。
云系统依赖虚拟化创建虚拟机等资源实例，但虚拟化削弱了可观测性，使平台级故障诊断十分困难。作者在华为云大规模生产系统上开展先导研究，发现功能相似的实例具有相似的通信与资源使用模式。基于此，论文将实例功能簇的识别形式化为聚类问题，提出非侵入式方案 Prism：先按通信模式把实例划分为粗粒度块，再在块内依据资源使用模式进一步分组，得到细粒度功能簇，从而降低数据噪声并高效处理海量实例。在华为云真实生产环境采集的两个数据集上，Prism 取得约 0.95 的 v-measure，优于现有最先进方案；论文还通过两个真实用例说明其可集成进监控系统以提升云可靠性。
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.
DOI 原文 · arXiv · PDF（开放获取） · DBLP
@inproceedings{abs-2308-07638,
title = {Prism: Revealing Hidden Functional Clusters from Massive Instances in Cloud Systems},
author = {Jinyang Liu and Zhihan Jiang and Jiazhen Gu and Junjie Huang and Zhuangbin Chen and Cong Feng and Zengyin Yang and Yongqiang Yang and Michael R. Lyu},
booktitle = {ASE},
year = {2023},
doi = {10.1109/ase56229.2023.00077}
}