Ledger Module Capacity Forecasting for Storage Cost Reduction
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Solution Overview
Problem
The rapid growth of information exceeds IT budgets for storage and management, leading to increased costs for storage resources, prompting the need for methods to reduce data storage, archive data in lower-cost devices, or decommission unused resources.
Innovation Solution
A system and technique for capacity forecasting that includes a ledger module executable by a processor unit, which stores resource data, creates archival transactions, links storage resources, and forecasts changes in storage capacity based on policy data, enabling planning scenarios and cost-capacity savings.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Loss of information
If data is accumulated and stored to accommodate growth, then information availability is improved, but storage costs increase
Solution Approach 1:
The system performs preliminary capacity forecasting by analyzing policy data and transaction data before actual storage decisions are made. The ledger module predicts future capacity requirements and identifies archival opportunities in advance, enabling proactive storage management that prevents costly last-minute expansions while ensuring information availability when needed.
Solution Approach 2:
The system implements continuous feedback loops where actual storage usage and capacity changes are recorded in the ledger and compared against forecasts. This feedback mechanism allows the system to learn from past predictions, refine forecasting accuracy, and dynamically adjust storage strategies to optimize the balance between information availability and storage cost.
2Quantity of substance
If storage resources are increased to accommodate data growth, then capacity is improved, but budget consumption increases
Solution Approach 1:
The ledger module performs preliminary capacity forecasting to predict future storage needs before budgets are committed. By analyzing policy data (retention periods, archival rules) and transaction data (data creation, deletion, migration patterns), the system generates forward-looking capacity forecasts that enable informed budgeting decisions, preventing overspending on unnecessary storage resources.
Solution Approach 2:
The system dynamically changes storage capacity parameters based on forecasted requirements rather than using static over-provisioning. The ledger module adjusts capacity allocations according to predicted data growth rates, policy changes, and transaction patterns, optimizing the balance between having sufficient capacity and minimizing budget consumption.
3Quantity of substance
If data is archived in lower-cost storage devices, then storage cost is reduced, but data access time may increase
Solution Approach 1:
The system applies local quality by differentiating storage locations based on data characteristics and access patterns. The ledger module analyzes policy data to identify which data should be archived and to which specific lower-cost storage devices, while maintaining fast access for frequently used data. This creates a heterogeneous storage architecture where different data types reside in optimally positioned storage locations.
Solution Approach 2:
The system introduces an intermediary layer (the ledger module with forecasting capabilities) that manages the complexity of data placement across multiple storage tiers. This intermediary automatically makes intelligent decisions about which data to archive and where to place it, eliminating the need for manual intervention and ensuring optimal balance between cost reduction and access time requirements.
4Adaptability or versatility
If manual storage management methods are used, then flexibility is maintained, but productivity decreases
Solution Approach 1:
The system implements self-service by enabling automated capacity forecasting and archival decision-making through the ledger module. The system automatically analyzes policy data and transaction data, generates capacity forecasts, identifies archival candidates, and executes storage management actions without requiring continuous manual intervention. This maintains organizational policies and flexibility while dramatically improving storage management productivity.
Solution Approach 2:
The ledger module serves multiple functions simultaneously: it forecasts capacity, analyzes policy compliance, identifies archival opportunities, tracks storage transactions, and generates recommendations. This multi-functional approach consolidates what would otherwise require multiple separate manual processes into a single automated system, improving productivity while maintaining the flexibility to handle diverse storage management scenarios.
Data Source
AI summary
According to one aspect of the present disclosure, a system and technique for capacity forecasting includes a host having a processor unit and a memory. Resource data associated with an environment is stored in the memory having inventory information of storage resources of the environment. A ledger module executable by a processor unit accesses policy data associated with data archival and creates an archival transaction. The ledger module targets data for the archival transaction, links at least one storage resource to the archival transaction, and forecasts a change in capacity of the linked storage resource for the archival transaction based on the policy data.