Rule-Based Drift Management for Complex System Configurations
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Solution Overview
Problem
Datacenter administrators face challenges in managing resource drift and maintaining consistency across a large number of software and hardware resources, as updates and advancements lead to siloed, dispersed, and complex deployments, causing system functionality and performance issues.
Innovation Solution
A rule-based approach for continuous drift and consistency management, involving the creation and application of drift and consistency rules to detect configuration drift and inconsistency, with composite templates and compliance scores to assess compliance, and notifications for timely corrective actions.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If resources are updated and improved over time with advancements in technology, then system functionality and performance are improved, but resource drift from gold standards occurs and system consistency deteriorates
Solution Approach 1:
The patent implements continuous monitoring that detects configuration drift and sends notifications to administrators. This feedback loop allows the system to identify when resources deviate from gold standards and enables corrective actions to restore consistency, thus resolving the contradiction between improving functionality and maintaining consistency.
Solution Approach 2:
The patent establishes gold standards representing optimal configurations before resources are updated. By having predefined reference configurations in place beforehand, the system can compare updated resources against these standards and detect drift early, preventing consistency deterioration while allowing functional improvements.
2Measurement precision
If continuous monitoring of all resources is implemented to detect drift, then detection precision is improved, but system complexity and computational overhead increase
Solution Approach 1:
The patent applies monitoring selectively to specific configuration attributes that are most critical for drift detection, rather than uniformly monitoring all possible attributes across all resources. This localized approach maintains high detection precision for key parameters while reducing overall system complexity and computational overhead.
Solution Approach 2:
The monitoring system is divided into modular components: gold standard definitions, continuous monitoring mechanisms, drift detection logic, and notification systems. This segmentation allows each component to be independently optimized and managed, reducing overall system complexity while maintaining comprehensive monitoring capabilities.
3Measurement precision
If comprehensive drift detection rules are applied to all resources, then detection precision is improved, but processing time and computational resources increase
Solution Approach 1:
The patent implements drift detection rules that focus on specific, critical configuration attributes rather than analyzing every possible attribute. This selective approach maintains high detection precision for important drift indicators while significantly reducing the time and computational resources required for processing.
Solution Approach 2:
The system applies drift detection continuously but selectively monitors only the most critical configuration attributes that are most likely to drift. This partial monitoring approach provides sufficient detection precision for key risks while avoiding the excessive processing time that would result from comprehensive analysis of all attributes.
Data Source
AI summary
Techniques are for rule-based continuous drift and consistency management for target systems. In one embodiment, a set of rules is stored in volatile or non-volatile store. The set of rules may include one or more drift rules and/or one or more consistency rules. A rule may be applied to one or more associated targets to detect drift or inconsistency. A drift rule identifies a set of one or more attributes and a source and may be applied by comparing a first configuration of the set of one or more attributes on an associated target with a second configuration of the set of one or more attributes on the source. A consistency rule may be applied to a composite target by comparing member targets that are grouped by target type. Notification data may be output if target drift or inconsistency is detected to alert a user.