Log Vector Clustering for SaaS Disaster Prediction
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Solution Overview
Problem
In IT environments, identifying the cause of failures such as natural disasters, network outages, and software failures in real-time from large volumes of logs generated by cloud application delivery systems is challenging due to the complexity of analyzing dynamic data, and existing methods lack precision and accuracy.
Innovation Solution
A disaster prediction system utilizing machine learning concepts, with an offline module that tokenizes and clusters logs to assign recovery solutions and an online module that categorizes new logs based on similarity to these clusters, enabling real-time disaster type identification and automatic recovery plan execution.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If brute force methods are used to scan logs to determine failure causes, then all logs can be analyzed, but the process is time-consuming and lacks precision
Solution Approach 1:
The system performs preliminary actions by pre-processing logs offline, tokenizing them into vectors, and clustering similar logs together before real-time analysis. This preliminary clustering allows the online module to quickly identify failure causes by comparing new logs against pre-grouped clusters, significantly reducing real-time analysis time while maintaining high precision through targeted keyword matching within relevant clusters.
Solution Approach 2:
The log analysis system is segmented into distinct modules: an offline module for pre-processing and clustering, and an online module for real-time analysis. The logs themselves are segmented into token vectors with specific dimensions. This segmentation allows each module to specialize - the offline module handles comprehensive processing while the online module handles rapid querying, resolving the contradiction between thorough analysis and speed.
2Measurement precision
If keyword driven methods are used with existing learning sets, then precision is improved, but the system lacks adaptability to new failure types
Solution Approach 1:
The system dynamically adapts to new failure types through its clustering mechanism. When new logs arrive, they are tokenized and compared against existing clusters. If new patterns emerge that don't fit existing clusters, the system can create new clusters, thereby dynamically updating its knowledge base. This dynamic structure maintains precision through keyword matching while simultaneously adapting to new failure types without requiring manual reconfiguration.
Solution Approach 2:
The system performs self-service by automatically learning from new log data. The offline module continuously processes new logs, updates clusters, and refines the knowledge base without external intervention. This self-learning capability allows the system to maintain high precision through accumulated knowledge while simultaneously adapting to new failure patterns, resolving the contradiction between precision and adaptability.
3Productivity
If large volumes of logs are analyzed in real-time, then comprehensive monitoring is achieved, but the computational complexity increases significantly
Solution Approach 1:
The system reduces real-time computational complexity by performing preliminary processing offline. Logs are tokenized into fixed-dimensional vectors and clustered before real-time analysis. This preliminary action transforms the complex task of analyzing raw log text into a simpler similarity matching problem against pre-computed cluster centers, enabling fast real-time processing without sacrificing comprehensive monitoring capability.
Solution Approach 2:
The system extracts essential features from logs by converting them into token vectors with specific dimensions. Only the relevant token frequencies and cluster assignments are retained for real-time analysis, while the original complex log text is processed offline. This extraction reduces the data volume and complexity that must be handled in real-time, enabling high productivity without proportionally increasing system complexity.
Data Source
AI summary
A method is provided to predict a disaster for a computer system based on logs. The method includes representing existing logs as first vectors by tokenizing the existing logs and partitioning the first vectors into clusters. The clusters represent disaster types. The method further includes selecting representative vectors for the clusters, representing a new log of the computer system as a second vector by tokenizing the new log, matching the second vector to a cluster by comparing the second vector and the representative vectors, and categorizing the new log as a disaster type represented by the cluster.