Root-Cause Analysis for Shared Backup Cost Anomalies
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Solution Overview
Problem
In shared data protection environments, unexplained spikes in cloud Cost of Goods Sold (CoGs) due to complex data pipelines are difficult to correlate with their root causes, lacking efficient data analytics for correlation and root-cause analysis.
Innovation Solution
A system and method for root-cause analysis that includes a backup/restore system with a processor and memory to identify and correlate anomalies by comparing predicted and actual cost/usage values of feature sets in storage server and backup/restore system telemetry data, using time-series forecasting models to optimize model usage.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If data analytics capabilities are enhanced to correlate anomalies with root causes, then diagnostic accuracy is improved, but system complexity increases
Solution Approach 1:
The patent introduces a root cause analysis module as an intermediary component that receives anomaly data from the backup system and correlates it with telemetry data from multiple sources. This mediator systematically processes and cross-references data to identify root causes, thereby improving diagnostic accuracy without requiring complete system redesign.
Solution Approach 2:
The analysis system is segmented into distinct functional modules: anomaly detection module, telemetry data collection module, root cause analysis module, and reporting module. Each module handles specific tasks independently, making the complex diagnostic process manageable and scalable while improving overall measurement precision through specialized processing.
2Productivity
If manual analysis of anomaly data is reduced, then operational efficiency is improved, but automation complexity increases
Solution Approach 1:
The system performs self-service by automatically detecting anomalies, collecting relevant telemetry data, analyzing root causes, and generating reports without requiring manual intervention. The root cause analysis module autonomously processes data from multiple sources and delivers actionable insights, significantly improving operational efficiency through automated decision-making.
Solution Approach 2:
The system implements feedback loops where anomaly detection triggers automated data collection and analysis, which then feeds back into the backup system for corrective actions. This closed-loop automation continuously monitors and adjusts operations, improving efficiency while managing complexity through systematic feedback mechanisms.
3Measurement precision
If comprehensive telemetry data collection is implemented, then root cause identification accuracy is improved, but data processing requirements increase
Solution Approach 1:
The patent extracts and collects only the specific telemetry data points relevant to anomaly investigation from multiple sources. The telemetry data collection module selectively gathers information based on anomaly type and potential root causes, reducing unnecessary data processing while maintaining high identification accuracy through targeted data extraction.
Solution Approach 2:
The system applies local quality by analyzing different aspects of telemetry data with appropriate depth based on the specific anomaly being investigated. Critical data points receive intensive analysis while less relevant data is processed lightly or ignored, optimizing data processing requirements while maintaining root cause identification accuracy through localized thoroughness.
Data Source
AI summary
A system for performing root-cause analysis of cost and/or usage anomalies in a shared data protection environment is presented. The shared backup environment includes a backup/restore system configured to backup data in a storage server and/or restore data from the storage server. The system is configured to perform the root-cause analysis based on storage server data and backup/restore system telemetry data.