In this era of cloud computing, users encounter the challenging task of effectively composing and running their applications on the cloud. By understanding user behavior in constructing applications and interacting with typical cloud infrastructures, cloud managers can develop better systems that improve the users’ experience. In this paper, we analyze a large dataset of a Google cluster to characterize the users into distinct groups of similar usage behavior. We used a wide range of measured metrics to model user behavior in composing applications from the perspective of actions around application architecting, capacity planning, and workload type planning and to model user interaction behavior around the session view. The trajectories of users’ actions are represented as sequences using categorical and proportional encoding schemes. We used techniques from the sequence analysis paradigm to quantify dissimilarity among users. We employed a robust cluster analysis procedure based on the agglomerative hierarchical methods to optimally classify users into 12 classes. We used a variety of formal indices and visual aids to confirm the quality and stability of the outcomes. By visual inspection, we regrouped the obtained clusters into 5 main groups that reveal interesting insights about the characteristics which underline different groups’ utilization behavior.

中文翻译：

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作为序列的谷歌用户：一项稳健的分层聚类分析研究

在这个云计算时代，用户面临着在云上有效编写和运行应用程序的挑战性任务。通过了解用户在构建应用程序和与典型云基础架构交互时的行为，云管理人员可以开发更好的系统来改善用户体验。在本文中，我们分析了 Google 集群的一个大型数据集，以将用户分为具有相似使用行为的不同组。我们使用了广泛的测量指标，从围绕应用程序架构、容量规划和工作负载类型规划的操作的角度对用户行为进行建模，并围绕会话视图对用户交互行为进行建模。用户动作的轨迹使用分类和比例编码方案表示为序列。我们使用序列分析范式中的技术来量化用户之间的差异。我们采用了基于凝聚分层方法的稳健聚类分析程序，以将用户最佳地分为 12 类。我们使用了各种正式指标和视觉辅助工具来确认结果的质量和稳定性。通过目视检查，我们将获得的聚类重新分组为 5 个主要组，这些组揭示了有关强调不同组使用行为的特征的有趣见解。