Proactive Stability Engine for Root-Cause Incident Triage
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Existing monitoring tools fail to identify the root cause of software or hardware errors in complex computing networks, leading to delayed incident resolution and increased downtime due to the inability to associate alerts with business workflows and perform automated system recovery.
Innovation Solution
A proactive engine that continuously monitors logs from various sources, standardizes data, and performs automated triage actions based on past incident profiles, using a Context Layer and profiler service to detect failures and implement tailored responses.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Loss of time
If existing monitoring tools are used to track errors in complex computing networks, then error logging is performed, but the root cause of errors cannot be identified and incident resolution is delayed
Solution Approach 1:
The system segments the complex network dependency graph into manageable components (upstream dependencies, current service, downstream dependencies) and analyzes error propagation through each segment. This allows the system to trace errors from their source through the dependency chain, identifying root causes without being overwhelmed by the complexity of the entire network.
Solution Approach 2:
The system performs preliminary actions by pre-building and maintaining a dependency graph that maps all upstream and downstream relationships between services. This pre-computed structural information is ready when errors occur, enabling immediate root cause analysis without delay. The system also pre-identifies potential failure propagation paths through the dependency network.
2Reliability
If manual triage processes are used to analyze errors, then human expertise can identify issues, but the process is costly in human time and prone to human error
Solution Approach 1:
The system implements self-service by enabling automated triage that independently analyzes errors, traces dependency chains, identifies root causes, and suggests remediation actions without requiring human intervention. The automated dependency analysis engine processes errors and generates diagnostic information autonomously, freeing human operators from routine triage tasks while maintaining high accuracy through systematic analysis of the pre-built dependency graph.
3Reliability
If APM solutions are deployed to monitor application performance, then service monitoring is performed, but the ability to identify possible interruptions or analyze remedies is lacking
Solution Approach 1:
The system adds another dimension to traditional APM by incorporating dependency topology analysis. Instead of only monitoring service metrics in isolation, the system overlays the dependency graph dimension, showing how errors propagate through upstream and downstream relationships. This additional dimensional context enables identification of root causes and prediction of potential interruptions by analyzing error patterns across the dependency network.
Data Source
AI summary
A computer-implemented method and system for averting possible disruptions to at least one software service is described. The method includes querying the data storage to identify a prevalence of errors associated with a particular software component or hardware component; supplementing identified errors with upstream and downstream contextual metadata and generating a profile of a current state of the plurality of software components and hardware components; matching the generated profile with a past profile, the past profile being associated with a desired automated triage action; and performing the automated triage action.