Datacenter Drift Visualization for Policy Violation Filtering
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Solution Overview
Problem
Current drift visualizations in datacenters are insufficient for identifying and communicating configuration changes and policy violations across millions of configuration items, as they either show limited data or list all changes, making it difficult for administrators to pinpoint relevant issues.
Innovation Solution
A system comprising a management server, agents for data collection, a change database, a data extractor for filtering data, and a policy datastore to continuously monitor and visualize changes in computing resource characteristics, providing a graphical representation of filtered data subsets based on data center policies.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Quantity of substance
If all change data from millions of configuration items is displayed, then complete data coverage is achieved, but the administrator cannot easily identify relevant changes or policy violations
Solution Approach 1:
The system segments the overwhelming volume of change data by organizing it into hierarchical groups (e.g., by configuration item type, policy category, or organizational structure). This allows administrators to navigate and focus on specific segments rather than viewing all changes simultaneously, making relevant changes identifiable while maintaining complete data coverage.
Solution Approach 2:
The system extracts and highlights only the changes that are relevant to current concerns or policy violations, separating them from the bulk of routine changes. This extraction mechanism allows administrators to focus on critical items while the complete dataset remains available for comprehensive analysis when needed.
2Ease of operation
If only a limited amount of data is shown in drift visualizations, then ease of analysis is improved, but complete identification of configuration trends and policy violations is compromised
Solution Approach 1:
The drift visualization system dynamically adjusts the amount and type of data displayed based on user interactions, current context, and identified patterns. As administrators explore the data, the system adapts to show more detailed information about emerging trends and policy violations, ensuring comprehensive identification without overwhelming the user from the start.
Solution Approach 2:
The system provides feedback mechanisms that allow administrators to drill down into aggregated data, request additional details, or adjust visualization parameters. This feedback loop ensures that while the initial view remains manageable for easy analysis, complete information about configuration trends and policy violations becomes accessible through interactive exploration.
3Quantity of substance
If current drift visualization tools list all changes, then data completeness is maintained, but the tools become insufficient for communicating trends and justifying corrective measures
Solution Approach 1:
The system adds new dimensions to the data presentation by incorporating temporal trends, policy compliance status, and impact assessments alongside the basic change listings. This multi-dimensional approach enables effective communication of trends and justification of corrective measures while maintaining complete data coverage, transforming raw change lists into actionable intelligence.
Data Source
AI summary
A system for drift visualization of change data of a datacenter is disclosed. The datacenter includes a plurality of configuration items. The system includes a management server in communication with the data center. The management server includes an agent configured to collect the change data from one or more of the plurality of configuration items. A change data store that is in communication with the management server is provided to store the change data. A data extractor in communication with the change data store is provided to retrieve the change data and providing a filtered subset of the change data to a display. A policy data store in communication with the data extractor is provided to enable the data extractor to filter the change data based on data center policy violations. The visualization depicts multiple dimensions of change-related data for the items in the datacenter in a unique and concise manner.