Template-Based Cloud Logging for High-Volume Log Analytics
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Solution Overview
Problem
Current log analytic tools are inefficient in handling the massive quantity of diverse computer performance logs produced by global cloud networks, failing to detect and mitigate complex performance anomalies in real-time due to their reliance on manual processing and off-line analysis of structured log lines.
Innovation Solution
A template-based logging system where clients send log template IDs with optional parameters instead of entire logging message strings, reducing processing overhead and optimizing storage, transfer, and search capabilities.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If conventional log analytic tools process massive quantities of diverse computer performance logs using manual processing and off-line analysis, then they can handle the volume of logs, but they fail to detect and mitigate complex performance anomalies in real-time
Solution Approach 1:
The patent segments log lines into structured components (timestamp, host, service, message type, message body) and further divides the processing into separate stages: log collection, log parsing, anomaly detection, and alerting. This segmentation allows each component to be optimized independently and enables real-time processing while maintaining high throughput.
Solution Approach 2:
The system performs preliminary actions by pre-defining anomaly detection rules and thresholds before logs arrive. The log parsing framework pre-structures log formats, and anomaly detection rules are pre-loaded, enabling immediate real-time analysis without delay for configuration or setup when anomalies occur.
2Measurement precision
If manual processing of computer performance logs is used, then detailed analysis can be performed, but the approach is not scalable to handle massive quantities of logs
Solution Approach 1:
The log parsing framework enables self-service by automatically interpreting log formats without human intervention. The system self-configures through predefined parsers that automatically recognize and extract structured data from logs, eliminating the need for manual analysis while maintaining high accuracy through consistent, rule-based processing.
Solution Approach 2:
The patent replaces manual mechanical processing with automated computational systems. Instead of human analysts manually reviewing logs, the system uses automated parsers, pattern recognition algorithms, and statistical anomaly detection mechanisms that process millions of logs simultaneously with consistent precision.
3Reliability
If off-line log analysis is performed to detect performance anomalies, then comprehensive analysis can be conducted, but the analysis is too slow to mitigate computer performance anomalies in real-time
Solution Approach 1:
The system maintains continuous real-time processing of logs through uninterrupted streaming analysis. The anomaly detection operates continuously as logs arrive, rather than performing periodic batch analysis. This continuous action enables immediate detection and response to anomalies while maintaining comprehensive analysis coverage.
Solution Approach 2:
The system implements periodic sampling and threshold-based triggering for anomaly detection. Rather than analyzing every log detail continuously, the system periodically checks for patterns and triggers deep analysis only when anomalies are detected, balancing real-time response with comprehensive analysis efficiency.
4Quantity of substance
If conventional log collection and analysis systems are deployed across large cloud networks, then log data can be collected, but redundant processing and excessive resource usage occur
Solution Approach 1:
The patent merges log collection, parsing, and initial analysis functions into a unified centralized framework. Instead of distributed processing across multiple independent systems, the framework consolidates these functions, enabling shared resources and eliminating redundant processing while maintaining comprehensive log analysis capability.
Solution Approach 2:
The log parsing framework provides universal functionality that handles multiple log formats and sources through a single unified system. The same framework processes logs from different services, hosts, and cloud providers, eliminating the need for separate specialized processing systems and reducing overall resource consumption through resource sharing.
Data Source
AI summary
Embodiments generate a log message in response to receiving a log event corresponding to a client. Embodiments identify a log message template that corresponds to the log event, the log message template including an identifier and zero or more required parameters. Embodiments generate the log message including the identifier and the zero or more parameters. Embodiments then transmit the log message to a logging server.