[网络领域顶会追踪系列#7][NSDI'22]CloudCluster：揭示云服务的功能结构

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CloudCluster: Unearthing the Functional Structure of a Cloud Service

背景&挑战

当客户要求云提供商提供很好的工具来管理云服务时，云提供商会因为缺少对云服务功能的视野而受到阻碍；一个提供商经常只知道服务的VM（虚拟机）放在哪、虚拟网络是怎么配置的、VM是如何被提供的、VM之间是怎么通信的。

工作

作者在本文中表明，使用VM与VM间的流量矩阵，可以挖掘出云服务的功能结构，并且使用它去给云服务管理江湖救急_（原文中的用词是aid cloud service management）_。

利用云服务在扩展性方面广为人知的设计模式（例如，复制、communication locality），作者表明对VM间的流量进行聚类可以得到云服务的功能结构。

作者的聚类算法CloudCluster必须克服由扩展性（云服务包含数以万计的VM）带来的挑战，并且必须对流量数据量和测量噪声的数量级的变化具有鲁棒性。为做到这一点，CloudCluster将特征缩放、维度减小和分层聚类做了创新的结合，实现了具有超过92%的同构性和完整性的聚类。

实验

展示了CloudCluster可以被用于探索减小用户开销的可能性、识别异常流量和潜在的错误配置。

原文摘要

In their quest to provide customers with good tools to manage cloud services, cloud providers are hampered by having very little visibility into cloud service functionality; a provider often only knows where VMs of a service are placed, how the virtual networks are configured, how VMs are provisioned, and how VMs communicate with each other. In this paper, we show that, using the VM-to-VM traffic matrix, we can unearth the_functional structure_of a cloud service and use it to aid cloud service management. Leveraging the observation that cloud services use well-known design patterns for scaling (e.g., replication, communication locality), we show that_clustering_the VM-to-VM traffic matrix yields the functional structure of the cloud service. Our clustering algorithm, CloudCluster, must overcome challenges imposed by scale (cloud services contain tens of thousands of VMs) and must be robust to orders-of-magnitude variability in traffic volume and measurement noise. To do this, CloudCluster uses a novel combination of feature scaling, dimensionality reduction, and hierarchical clustering to achieve clustering with over 92% homogeneity and completeness. We show that CloudCluster can be used to explore opportunities to reduce cost for customers, identify anomalous traffic and potential misconfigurations.