Monitoring for cloud is the key technology to know the status and the availability of the resources and services present in the current infrastructure. However, cloud monitoring faces a lot of challenges due to inefficient monitoring capability and enormous resource consumption. We study the adaptive monitoring for cloud computing platform, and focus on the problem of balancing monitoring capability and resource consumption. We proposed HSACMA, a hierarchical scalable adaptive monitoring architecture, that (1) monitors the physical and virtual infrastructure at the infrastructure layer, the middleware running at the platform layer, and the application services at the application layer; (2) achieves the scalability of the monitoring based on microservices; and (3) adaptively adjusts the monitoring interval and data transmission strategy according to the running state of the cloud computing system. Moreover, we study a case of real production system deployed and running on the cloud computing platform called CloudStack, to verify the effectiveness of applying our architecture in practice. The results show that HSACMA can guarantee the accuracy and real-time performance of monitoring and reduces resource consumption.

中文翻译：

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HSACMA：分层可扩展的自适应云监控架构

云监控是了解当前基础架构中存在的资源和服务的状态和可用性的关键技术。然而，由于监控能力低效、资源消耗巨大，云监控面临诸多挑战。我们研究了云计算平台的自适应监控，重点解决了监控能力和资源消耗的平衡问题。我们提出了HSACMA，一种分层可扩展的自适应监控架构，它（1）监控基础设施层的物理和虚拟基础设施，平台层运行的中间件，以及应用层的应用服务；(2) 实现了基于微服务的监控的可扩展性；(3)根据云计算系统的运行状态自适应调整监测间隔和数据传输策略。此外，我们研究了一个真实的生产系统部署并运行在CloudStack云计算平台上的案例，以验证我们的架构在实践中的有效性。结果表明，HSACMA可以保证监控的准确性和实时性，减少资源消耗。