Auto-Healing Storage System for Deviation Remediation
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Solution Overview
Problem
Current data management storage solutions face challenges in efficiently identifying and automatically remediating deviations from best practices, leading to prolonged downtime and revenue loss due to complex infrastructure and the need for manual intervention by administrative users.
Innovation Solution
The implementation of an auto-healing system that uses machine learning classification models and rule sets to proactively monitor data management storage systems, identify deviations, and automatically implement remediations based on community wisdom, enabling real-time health analysis and notifications.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If manual monitoring and remediation of storage system deviations is used, then operational control and flexibility are maintained, but downtime increases and productivity decreases
Solution Approach 1:
The storage system automatically monitors itself for deviations from best practices and applies remediations without human intervention. The system evaluates rules, identifies deviations, selects appropriate remediations, and implements fixes autonomously, enabling self-healing capabilities that reduce both downtime and operational burden
Solution Approach 2:
The system continuously monitors storage system state, compares it against best practice rules, and automatically responds to deviations. This closed-loop feedback mechanism enables real-time detection and remediation of issues, improving both availability and response speed compared to manual processes
2Productivity
If automated remediation systems are implemented, then productivity and response speed improve, but device complexity increases
Solution Approach 1:
The automated remediation system is divided into distinct modular components: rule evaluation modules that assess specific conditions, remediation selection modules that choose appropriate fixes, and execution modules that apply corrections. This segmentation manages complexity by organizing functions into independent, manageable units
Solution Approach 2:
Rule sets act as intermediaries between monitoring functions and remediation actions. These pre-defined rules translate complex system states into actionable decisions, simplifying the automation logic while maintaining comprehensive monitoring and response capabilities
3Measurement precision
If comprehensive rule evaluation is performed, then measurement precision of deviations improves, but use of energy and processing resources increases
Solution Approach 1:
The system evaluates rules selectively based on triggered events rather than continuously monitoring all parameters. When specific events occur, relevant rule sets are activated to assess deviations, providing sufficient detection accuracy while avoiding the excessive resource consumption of exhaustive continuous evaluation of all possible conditions
Data Source
AI summary
Systems and methods for automated remediation of deviations from best practices in the context of a data management storage system are provided. Deployed assets of a storage solution vendor may periodically deliver telemetry data to the vendor. The telemetry data may be processed by an AIOps platform to perform predictive analytics and arrive at “community wisdom” from the vendor's installed base. In one embodiment, an insight-based approach is used to facilitate risk detection and remediation including proactively addressing deviations from best practices before they turn into more serious problems. Based on the community wisdom and making a rule set and a remediation set derived therefrom available for use by auto-healing service associated with a customer's storage system, a risk (e.g., a deviation from a best practice) to which the storage system is exposed may be determined and a corresponding remediation may be deployed to address or mitigate the risk.