Non-Invasive Storage Capacity Planning via Intermediary Data Collection
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Solution Overview
Problem
Conventional storage management tools lack the ability to provide users with actionable, predictive data for future storage capacity planning, relying on invasive software and limited analysis capabilities that do not account for business metrics, leading to inefficient storage resource allocation and increased costs.
Innovation Solution
A non-invasive data discovery and collection mechanism, adaptive grouping, flexible analysis, and predictive modeling system that incorporates business metrics to forecast future storage needs, allowing users to create and modify scenarios for accurate capacity planning without requiring additional software on the systems being measured.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Difficulty of detecting and measuring
If conventional storage management tools use invasive software to collect data, then data collection capability is improved, but system complexity and deployment difficulty increase
Solution Approach 1:
The patent introduces an intermediary component that acts as a bridge between the storage systems and the management tool. This intermediary collects and standardizes data from multiple storage systems using common protocols, eliminating the need for invasive software on each system. The intermediary layer simplifies the overall architecture while maintaining comprehensive data collection capabilities.
Solution Approach 2:
The patent replaces the mechanical approach of installing invasive software agents on storage systems with a protocol-based communication mechanism. By using standardized interfaces and network protocols, the system achieves data collection without physical or software intrusion, reducing complexity while maintaining measurement capability.
2Quantity of substance
If storage capacity is continuously increased to meet growing demands, then storage availability is improved, but costs increase
Solution Approach 1:
The patent implements predictive analytics and forecasting capabilities that analyze historical usage patterns and project future storage needs. This preliminary action allows organizations to plan storage expansion proactively, acquiring capacity only when truly needed rather than continuously over-provisioning. The system provides early warnings and scenario planning to optimize the timing and amount of storage purchases.
Solution Approach 2:
The patent establishes continuous monitoring and feedback loops that track actual storage usage against predicted demands. This feedback mechanism enables dynamic adjustment of storage provisioning strategies, allowing organizations to right-size their storage investments based on real-world usage patterns rather than static over-provisioning, thereby reducing unnecessary costs.
3Ease of operation
If basic monitoring tools are used to track current storage needs, then ease of operation is improved, but predictive capability and business metric integration are reduced
Solution Approach 1:
The patent designs a multi-functional platform that combines simple monitoring capabilities with advanced predictive analytics and business metric integration in a single unified system. The interface maintains ease of use through standardized views and reports, while backend processes automatically perform complex predictive modeling and correlate storage data with business metrics such as revenue, customer transactions, and operational KPIs.
Solution Approach 2:
The patent implements dynamic adaptability where the system automatically adjusts its analysis depth and predictive modeling based on user needs and data availability. The platform can operate in simple monitoring mode for basic users while providing sophisticated predictive analytics and business metric correlation for advanced users, all through a single adaptable interface that maintains ease of operation across different complexity levels.
Data Source
AI summary
A system and methods for capacity planning of electronic systems storage are provided. Features include a non-invasive data discovery and collection mechanism, an adaptive grouping mechanism, a flexible analysis mechanism, predictive modeling and forecasting mechanisms, and a business metric and correlation mechanism. A discovery engine that ascertains the availability of collectable entities. A collection engine in turn gathers information from and about entities discovered by the discovery engine. A grouping module groups together entities according to parameters specified by the user. An analysis and forecasting module allows a user to create, modify and save forecast scenarios from which a model is generated by a modeling module. Correlations can be identified between specified key business metrics and historical data. Once a model has been created, a forecast is generated and can then be analyzed using Bold Freehand Extrapolation, time-series analysis, and business trend-based forecasting.