Policy Engine for Automated System Management
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Solution Overview
Problem
Administrators face challenges in achieving optimal and predictable performance in data storage systems due to the complexity of understanding interactions among various tuning mechanisms and management operations, leading to sub-optimal system performance and increased operational costs.
Innovation Solution
A system management framework that includes a policy engine with a situational analysis framework, which automatically identifies and deploys optimal policy sets and management operations best practices by analyzing the situational state space of the information management system, using a learning module and production module to monitor and adjust system settings based on changing conditions.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If administrators manually adjust tuning mechanisms and management operations to achieve optimal performance, then system performance can be optimized, but the complexity of understanding interactions among various choices increases and requires expert knowledge
Solution Approach 1:
The system performs self-adjustment of tuning mechanisms and management operations through automated policies that monitor system state and automatically modify configurations without requiring administrator intervention or expert knowledge of the complex interactions between parameters
Solution Approach 2:
An automated policy engine acts as an intermediary between system administrators and the complex tuning mechanisms, translating high-level performance goals into specific configuration adjustments while shielding administrators from the underlying complexity of parameter interactions
2Productivity
If administrators become experts at setting policies and determining management operations best practices, then system performance can be optimized, but total cost of operation increases significantly due to the expertise and attention required
Solution Approach 1:
The system automatically optimizes its own performance through embedded policies and automated decision-making algorithms, eliminating the need for expensive expert administrators while maintaining optimal system performance through continuous self-monitoring and self-adjustment
3Productivity
If low-level tuning mechanisms and management operations are used to adjust system parameters, then system performance can be improved, but it remains difficult to adjust effectively to changing workloads and performance targets
Solution Approach 1:
The system implements dynamic adjustment of tuning mechanisms through policies that continuously monitor changing system state and workload conditions, automatically adapting configurations in real-time to maintain optimal performance across varying operational scenarios
Solution Approach 2:
Automated policies incorporate feedback loops that continuously monitor system performance metrics and workload changes, using this information to dynamically adjust management operations and tuning parameters to achieve and maintain performance targets under changing conditions
Data Source
AI summary
Methods and systems for use in providing system management services are disclosed. In at least one embodiment, a method and system may comprise receiving a management operation request at a recommendation service. Based on the management operation request and recommendation control policies, management operation recommendations associated with an information management system are determined.In at least one embodiment, a method and system may comprise receiving information associated with an information management system at a learning service. Based on the information, dimensions of a situational state space characterizing operating conditions of the information management system are determined. Best practices for at least one state of the situational state space are determined.