Root cause analysis (RCA) is critical for maintaining the availability and efficiency of cloud computing systems. However, identifying root causes from the large-scale, high-dimensional monitoring data generated by these complex environments is a significant challenge. Current approaches often rely on time-consuming manual analysis to ensure flexibility and reliability, while recent automated methods lack the crucial insights provided by domain experts. To bridge this gap, we propose RCInvestigator, a visual analytics system that facilitates interactive root cause investigation by establishing a tight collaboration between human experts and machine analysis. Our approach addresses three key challenges: a) modeling databases for the root cause investigation, b) inferring root causes from large-scale time series, and c) building comprehensible investigation results. We demonstrate the effectiveness and utility of RCInvestigator through two real-world case studies, which received positive feedback from domain experts.

Ensuring high server availability and capacity in cloud computing platforms is a critical challenge, particularly in managing node recovery leading to capacity improvement. Microsoft Azure, a leading cloud platform, requires a robust approach to handle capacity at fleet without significantly impacting customer VM allocation failures and maintaining available capacity customers. This paper presents a novel, automated, and intelligent capacity management system ‘Stratus' that integrates machine learning-based capacity health prediction into Azure platform services to optimize node recovery policies dynamically. Our approach improves capacity allocation failures, server availability, and enhances the overall efficiency of cloud platform operations by integrating predictive analytics with policy-driven automation. Experimental evaluations show that Stratus reduced capacity-related allocation failures by 12% for General Purpose VMs, 16% for L-Series and 19% for M-Series. Also, Stratus recovered significantly more nodes compared to legacy system, 220% for General purpose VM types, 133% for L-Series and 143% for M-Series VM types successfully, proving its effectiveness in large-scale cloud environments.

In cloud systems, an accurate capacity planning is very important for cloud provider to improve service availability. Traditional methods simply predicting "when the available resources is exhausted" are not effective due to customer demand fragmentation and platform allocation constraints. In this paper, we propose a novel prediction approach which proactively predicts the level of resource allocation failures from the perspective of low capacity status. By jointly considering the data from different sources in both time series form and static form, the proposed approach can make accurate LCS predictions in a complex and dynamic cloud environment, and thereby improve the service availability of cloud systems. The proposed approach is evaluated by real-world datasets collected from a large scale public cloud platform, and the results confirm its effectiveness.