Storage Volume Compression Ranking for Space and Cost Savings
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Solution Overview
Problem
Current storage technologies lack an efficient method to prioritize and schedule data compression across multiple storage volumes, leading to suboptimal storage space savings and cost savings, as existing methods do not effectively assess and rank the potential benefits of compression across different storage environments.
Innovation Solution
A method that examines information from multiple storage volumes to predict storage space savings and compression cost savings, ranking volumes based on these predictions and scheduling compression accordingly, using a system that includes a manager system to perform terabyte storage volume savings and cost savings predictions, and scheduling data compression based on these rankings.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Quantity of substance
If data compression is applied to all storage volumes without prioritization, then storage space savings may be achieved, but computational resources are wasted on volumes that provide minimal benefit
Solution Approach 1:
The system performs preliminary examination and prediction of compression benefits for each storage volume before actually applying compression. By predicting storage space savings and cost savings in advance, the system identifies which volumes are most likely to benefit from compression, thereby avoiding wasteful application of compression to volumes that would provide minimal benefit.
Solution Approach 2:
The system changes the parameter of compression application from a uniform approach (applying to all volumes) to a selective approach based on predicted savings parameters. By ranking volumes according to predicted storage space savings and cost savings, the system dynamically determines which volumes should be compressed, optimizing both space utilization and resource consumption.
2Quantity of substance
If compression is applied to storage volumes with high priority ranking, then maximum storage space savings are achieved, but the complexity of assessing and ranking volumes increases
Solution Approach 1:
The assessment process is segmented into distinct functional components: an examining component that collects volume information, a prediction component that estimates storage space savings, another prediction component that estimates cost savings, and a ranking component that prioritizes volumes. This segmentation allows each component to perform its specific function efficiently, managing overall system complexity through modular design.
Solution Approach 2:
The system introduces intermediary prediction mechanisms that act as mediators between the raw volume information and the final compression decisions. These prediction components translate complex volume characteristics into simplified savings estimates, which then guide the ranking and compression prioritization process without requiring direct complex analysis of all volume parameters.
3Loss of energy
If compression scheduling is based on predicted cost savings per terabyte, then operational costs are reduced, but the precision of cost prediction must be high to avoid suboptimal decisions
Solution Approach 1:
The system implements a feedback mechanism where actual compression results are compared against predicted savings. This feedback loop allows the system to refine its prediction algorithms over time, improving the precision of cost predictions by learning from actual outcomes and adjusting prediction parameters accordingly.
Solution Approach 2:
The system performs preliminary cost savings predictions for all volumes before committing to compression actions. By estimating cost savings in advance and using these predictions to guide scheduling decisions, the system can make informed choices about which volumes to compress first, minimizing operational costs even if individual predictions have some uncertainty.
Data Source
AI summary
Methods, computer program products, and systems are presented. The method computer program products, and systems can include, for instance: examining information of first through Nth storage volumes and based on the examining providing for each storage volume of the first through Nth storage volumes a predicted storage space savings value, the predicted storage space savings value indicating a predicted terabyte volume of storage space savings producible by performance of data compression of data stored on the storage volume; predicting a per terabyte compression cost savings associated with compressing one or more storage volume of the first through Nth storage volumes, and providing a ranking of storage volumes of the first through Nth storage volumes based on the examining and the predicting; and scheduling a compression of storage volumes of the first through Nth storage volumes based on the ranking of storage volumes of the first through Nth storage volumes.