VMI Schedule Detection via Log Pattern Extraction
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Solution Overview
Problem
In cloud environments, virtual machine instance (VMI) scheduling is hindered by the complexity of logs and root cause identification, making it difficult to determine if a node is stable and if VMI creation is successful, especially due to issues like controller pods crashing, network problems, and storage outages.
Innovation Solution
A method involving a pre-analysis process that collects logs from multiple sources, formats data using a data formatter, reduces dimensionality, and extracts key patterns to separate normal and abnormal statuses, followed by a post-analysis process that uses a threshold formatter for health checks to predict VMI creation success.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If manual log analysis is performed to debug pending VMI issues, then root cause identification can be achieved, but time consumption and operational complexity increase significantly
Solution Approach 1:
The system performs preliminary analysis by collecting logs from multiple sources before VMI scheduling occurs, extracting key patterns, and establishing health check thresholds in advance. This enables proactive detection of potential issues before they prevent VMI creation, eliminating the need for manual post-mortem analysis and significantly reducing troubleshooting time while maintaining accurate root cause identification.
Solution Approach 2:
The system enables self-service monitoring by automatically collecting logs, extracting patterns, and generating health check results without requiring manual intervention from site reliability engineers. The automated health check process independently identifies node stability issues and VMI creation failures, freeing operational staff from time-consuming manual analysis while maintaining comprehensive monitoring capabilities.
2Measurement precision
If comprehensive log collection from multiple sources is performed, then detection accuracy improves, but system complexity and processing difficulty increase
Solution Approach 1:
The system extracts only the most critical information from comprehensive log collections by identifying and isolating key patterns that indicate node stability or VMI creation issues. The pattern extraction process filters out redundant and irrelevant log entries, presenting only the essential diagnostic information needed for health checks while maintaining high detection accuracy and reducing processing complexity.
Solution Approach 2:
The log analysis process is segmented into distinct functional stages: log collection from multiple sources, data formatting, pattern extraction, and health check evaluation. Each stage processes information independently and passes results to the next stage, making the overall complex system manageable and easier to implement while maintaining comprehensive monitoring capabilities.
3Reliability
If real-time health check implementation is performed, then VMI creation success prediction improves, but computational resource consumption increases
Solution Approach 1:
The system applies partial action by performing health checks only on critical parameters and patterns that directly impact VMI creation success, rather than continuously analyzing all possible system states. The pattern extraction process identifies and monitors only the most relevant indicators of node stability and resource availability, reducing computational resource consumption while maintaining reliable prediction of VMI creation outcomes.
Data Source
AI summary
In several aspects for detecting a computing system resource schedule, a computing device performs a pre-analysis process utilizing a collected log from multiple sources. The pre-analysis process including a data formatter that identifies data based on a metric, and a dimensionality reduction process that distributes the data into an n-dimensional space. Key patterns are extracted to separate a normal status and an abnormal status for the extracted key patterns. A post-analysis process is performed on the extracted key patterns utilizing a threshold formatter to identify a threshold for a health check.