HSACMA: a hierarchical scalable adaptive cloud monitoring architecture
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HSACMA: a hierarchical scalable adaptive cloud monitoring architecture Rui Wang1 · Shi Ying2
· Meiyan Li1 · Shun Jia1
© Springer Science+Business Media, LLC, part of Springer Nature 2020
Abstract 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. Keywords Cloud monitoring · Hierarchy · Scalability · Adaptability
Shi Ying
[email protected] Rui Wang [email protected] Meiyan Li [email protected] Shun Jia [email protected] 1
College of Energy and Mining Engineering, Shandong University of Science and Technology, Qingdao, China
2
School of Computer Science, Wuhan University, Wuhan 430072, China
Software Quality Journal
1 Introduction Cloud computing is a new type of computing and service model, which is based on technologies such as distributed computing (Thain et al. 2005), grid computing (Berman and Fox 2003), parallel computing (Chen 2011), and virtualization (Barham et al. 2003). It establishes a sharing pool of computing resources to provide users with a wide range of cloud services for computing, storage, database, analytic, application, deployment, and so on in a pay-as-you-go style. According to NIST (Mell and Grance 2011), cloud computing can provide three different service models: Infrastructure as a Service (IaaS), Platform as a Service (PaaS), and Software as a Service (SaaS). In the cloud computing platform, the application system has strict requirements for performance, such as Integrated Disaster Reduction Application System (IDRAS) (Wang et al. 2017), and it has a series of Web components with independent functions. When the disaster occurs, the system visualizes the risk and loss of natural disasters from two dimensions of space and time in the shortest possible time, and provides direct information for the disaster management. However, the diversity of applications and the dynamic nature of the deployment environment ofte
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