Data Archiving Plan Simulation for Granular Cloud Storage Cost Control
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Solution Overview
Problem
Organizations face challenges in efficiently managing and protecting large volumes of data while minimizing costs and maintaining compliance with retention objectives, as existing data archiving methods often lack flexibility and do not account for granular data attributes.
Innovation Solution
A data storage management system that enables users to model and simulate archiving plans using indexing and content mining, allowing for granular data selection based on various criteria, and implements archiving jobs to move data to lower-cost archive storage while maintaining compliance.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Loss of energy
If data is archived to lower-cost storage, then storage costs are reduced, but data accessibility and retrieval time increase
Solution Approach 1:
The system segments data into different archiving groups based on granular attributes (metadata and content attributes) rather than treating all data uniformly. This allows selective archiving of only those data portions that meet specific criteria, keeping frequently accessed data in primary storage while moving less critical data to archive storage, thus balancing cost reduction with accessibility needs.
Solution Approach 2:
The archiving system is dynamic and adaptable, using machine learning models to continuously learn from data access patterns and automatically adjust archiving decisions. The system can dynamically determine whether to archive or retain data based on evolving access patterns, ensuring that data accessibility requirements are met while maximizing cost savings.
2Manufacturing precision
If granular data selection is implemented using indexing and content mining, then archiving precision is improved, but system complexity increases
Solution Approach 1:
The system introduces an intermediary machine learning model that acts as a bridge between raw data attributes and archiving decisions. This model processes metadata and content attributes, learns patterns from data access behavior, and outputs archiving recommendations, thereby simplifying the overall system architecture while enabling precise granular data selection.
Solution Approach 2:
The system employs self-service mechanisms through automated machine learning models that continuously learn from data access patterns without requiring manual intervention. The models automatically update their understanding of data priorities and archiving criteria, reducing the need for complex manual configuration and maintenance while maintaining high archiving precision.
3Productivity
If data is archived without modeling and simulation, then archiving speed is improved, but decision-making quality deteriorates
Solution Approach 1:
The system performs preliminary action by conducting archiving simulations and cost modeling before actual archiving operations. These simulations predict the outcomes of different archiving strategies, allowing stakeholders to evaluate potential cost savings and accessibility impacts beforehand, thereby enabling informed decision-making while maintaining efficient execution speeds.
Data Source
AI summary
The disclosed data storage management system enables data owners to model the costs and attributes of archiving their data and to readily capture and implement one or more resultant archiving plans. Modeling enables data owners to make informed choices about cost profiles before data is actually archived. Archiving plans devised according to these choices are intended to save on data storage costs and provide a compliance-ready data archive in cloud storage repository(ies). Armed with archiving simulations supplied by the illustrative data storage management system, a data owner may control data placement to predict costs, free up primary storage, and move inactive data to less expensive archive storage. Preferably, the disclosed system is implemented as a software-as-a-service (SaaS) solution, and the accompanying archive storage is implemented as a cloud storage service, but the invention is not limited to SaaS or to cloud-based data archives.