Dynamic Policy Prioritization for Storage Volume Optimization
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Solution Overview
Problem
Current Information Lifecycle Management (ILM) systems rely on manual or crude rules and scripts for data management, which fail to optimize storage performance and capacity, leading to inefficiencies in data movement and cost management.
Innovation Solution
A computer-implemented method and system that dynamically prioritizes storage policies based on analyzed storage and capacity, translating business rules into actionable adjustments such as tiering, thinning, and compressing storage volumes to optimize storage performance and cost.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of operation
If manual or crude rules and scripts are used for ILM policy implementation, then ease of operation is improved, but productivity and optimization capability deteriorate
Solution Approach 1:
The system enables self-service by automatically analyzing storage capacity, dynamically prioritizing policies, and translating business rules into actions without requiring manual intervention. The cognitive computing system autonomously optimizes data movement decisions based on current storage environment analysis.
Solution Approach 2:
The system implements dynamic policy prioritization where policy execution order is not fixed but adapts based on real-time storage capacity analysis. The system continuously re-evaluates and re-prioritizes policies according to current storage conditions, enabling flexible optimization.
2Productivity
If dynamic policy prioritization and cognitive translation of business rules are implemented, then productivity and optimization capability are improved, but device complexity increases
Solution Approach 1:
The cognitive computing system serves multiple functions: it analyzes storage capacity, prioritizes policies dynamically, translates business rules into executable actions, and executes data movement operations. This multi-functional approach consolidates complexity into a single intelligent system rather than requiring separate systems for each function.
Solution Approach 2:
The system introduces a cognitive computing intermediary that sits between business rules and execution. This intermediary translates high-level business rules into specific actionable policies, shielding the complexity of policy management from both users and execution systems while maintaining optimization capability.
3Device complexity
If crude rules and scripts are used for data management, then device complexity is reduced, but loss of information and optimization capability worsen
Solution Approach 1:
The system continuously monitors storage capacity and performance metrics, using this feedback to dynamically adjust policy prioritization. The feedback loop ensures that data movement decisions are based on current actual conditions rather than static rules, preventing information loss and maintaining optimization.
Solution Approach 2:
The system performs preliminary analysis of storage capacity and conditions before executing data movement actions. By analyzing the storage environment in advance and prioritizing policies based on current conditions, the system ensures that appropriate actions are taken to prevent information loss before it occurs.
Data Source
AI summary
A computer-implemented method of information lifecycle management is disclosed. The computer-implemented method includes reading, by a data processing system of a storage environment, business rules and policies for managing data in storage volumes of the storage environment, the policies being based on the predetermined business rules, and analyzing, by the data processing system, available storage and capacity in the storage environment. The computer-implemented method further includes dynamically prioritizing, by the data processing system, the policies based, at least in part, on results of the analyzing, resulting in prioritized policies, cognitively translating, by the data processing system, one or more of the predetermined business rules into action(s) against one or more of the storage volumes based, at least in part, on the prioritized policies, and executing, by the data processing system, the action(s).