Storage Cost Optimization via Data Volume Monitoring
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Solution Overview
Problem
Companies face high storage costs due to the accumulation of 'dark data' in IoT systems, where data value is unclear, leading to increased costs over time, inhibiting industrial development.
Innovation Solution
A storage system that monitors write and read data amounts, calculates storage costs based on usage, and applies compression techniques to optimize costs by evaluating data value and reducible data amounts over time.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Quantity of substance
If data is stored in an IoT system, then data accumulation and availability are improved, but storage costs increase over time
Solution Approach 1:
The system performs preliminary actions by monitoring storage amounts and read amounts before costs become excessive. It calculates storage costs in advance based on monitored usage patterns, allowing companies to take proactive measures to optimize storage costs before they accumulate significantly over time.
Solution Approach 2:
The system implements continuous feedback by monitoring both storage amounts and read amounts, then calculating storage costs based on this monitored data. This feedback mechanism allows the system to adjust and optimize storage strategies based on actual usage patterns, preventing cost escalation while maintaining necessary data availability.
2Duration of action of stationary object
If storage period is extended, then data availability is improved, but storage costs increase
Solution Approach 1:
The system uses feedback by continuously monitoring storage amounts and read amounts over extended periods. It calculates storage costs based on this longitudinal data, enabling optimization of storage strategies as the storage period extends, thereby preventing linear cost increases even as data availability is maintained.
Solution Approach 2:
The system applies dynamics by adjusting storage cost calculations based on actual monitored usage patterns over time. Rather than static cost models, the system dynamically adapts to changing storage and read patterns, allowing extended storage periods to be managed more efficiently with costs that reflect actual data value and usage.
3Quantity of substance
If data volume is increased, then data completeness is improved, but storage costs increase
Solution Approach 1:
The system implements feedback by monitoring both storage amounts and read amounts, then calculating storage costs based on this dual-monitoring data. This allows the system to identify which portions of increased data volume are actually being utilized, enabling optimization strategies that maintain data completeness while reducing costs for unused data.
Solution Approach 2:
The system applies local quality by differentiating between various portions of stored data based on their read patterns and calculated costs. It can apply different storage strategies to different data segments - maintaining high availability for frequently read data while optimizing or archiving less frequently accessed data, thereby reducing overall storage costs while preserving data completeness.
Data Source
AI summary
An object of the invention is to optimize a storage cost for data. There is provided a storage system including a storage device, a memory, and a processor configured to control input and output of data to and from the storage device. The processor monitors a storage amount that is at least one of a write amount (a total amount of data received as a write target) and a physical use amount (a total amount of data physically stored in the storage device), and a read amount (a total amount of data that is read), and calculates a fee as a storage cost that is a cost related to use of the storage device in a target period, based on a storage amount and a read amount in the target period in accordance with a monitoring result.