Unified Triage Module for Automated Server Cluster Error Resolution
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Solution Overview
Problem
Conventional methods for triaging computer system performance in multi-tenant cloud-based architectures are tedious and cumbersome, particularly for large server clusters, as they require manual attention to routine errors across multiple sources of data.
Innovation Solution
A machine learning algorithm is implemented to automatically aggregate error logs, identify patterns, and propose solutions based on previous errors, enabling unified triaging and self-service corrective actions without human intervention.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If manual triaging methods are used to monitor and resolve errors in large server clusters, then operators can address errors with human judgment, but the process becomes tedious and time-consuming
Solution Approach 1:
The system enables self-service by automatically detecting errors, retrieving relevant logs, identifying patterns, and proposing resolutions without human intervention. The machine learning model autonomously performs triaging tasks that would otherwise require manual operator intervention, allowing the system to resolve routine errors independently
Solution Approach 2:
The system performs preliminary actions by pre-configuring error detection rules, log collection mechanisms, and pattern recognition models before errors occur. When errors happen, the system has already prepared the infrastructure and algorithms needed for rapid automated response, eliminating the need for manual setup and analysis
2Productivity
If automated systems are implemented to reduce manual triaging effort, then processing speed increases, but system complexity increases
Solution Approach 1:
The system achieves universality by creating a multi-functional automated triaging platform that can handle multiple error types, collect various log formats, perform pattern recognition, and generate resolutions across different server clusters. This single unified system replaces multiple manual processes and tools, managing complexity through consolidation rather than proliferation of separate systems
Solution Approach 2:
The machine learning model acts as an intermediary between raw error data and human operators. It receives complex multi-source error data, processes it through learned patterns, and outputs simplified actionable insights. This intermediary layer manages the complexity transformation, converting chaotic raw data into structured recommendations without requiring humans to directly handle the complexity
3Measurement precision
If comprehensive log collection is performed from multiple sources to improve error analysis accuracy, then diagnostic precision improves, but data processing overhead increases
Solution Approach 1:
The system applies partial action by selectively collecting and processing only the most relevant logs based on error type and severity. Rather than uniformly processing all possible logs, the system intelligently filters and prioritizes log sources, processing only the subset necessary for accurate diagnosis while avoiding unnecessary computational expenditure on irrelevant data
Data Source
AI summary
Methods and systems are provided for automatically triaging a server cluster of the type including a plurality of linked servers each running a plurality of processes. The method includes: detecting at least one failed process; automatically transmitting an electronic alert message embodying a first error code indicative of the failed process to a unified triage module including a processor and an updatable index table; applying, by the processor, the first error code to the index table. If a matching error code corresponding to the first error code is found in the index table, retrieving a solution code from the index table associated with the matching error code and automatically restarting the failed process using the solution code without human intervention.