Storage Volume Compression Scheduling by Predicted Savings Value
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Solution Overview
Problem
Current data compression methods lack an efficient strategy for prioritizing and scheduling compression across multiple storage volumes to maximize storage space savings and cost savings in storage environments.
Innovation Solution
A method that examines each storage volume to predict terabyte volume savings and per terabyte compression cost savings, ranking volumes based on these predictions and scheduling compression accordingly to optimize data compression across storage volumes.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Quantity of substance
If data compression is applied to all storage volumes, then storage space savings are achieved, but system resources are wasted on compressing volumes that do not benefit from compression
Solution Approach 1:
The system performs preliminary examination of storage volume information and predicts compression space savings before executing compression. This allows identification of volumes that are suitable candidates for compression, avoiding waste of processing resources on volumes that would not benefit from compression.
Solution Approach 2:
The system automatically schedules compression tasks based on predicted savings and current system conditions without requiring manual intervention. The compression scheduling system monitors and adjusts compression tasks autonomously to optimize resource utilization while maximizing storage space savings.
2Productivity
If compression is scheduled without prioritization, then all volumes are processed, but time is wasted processing low-value volumes first
Solution Approach 1:
The system changes the scheduling parameters by introducing priority levels based on predicted compression savings. Volumes are ranked according to their potential space savings, and the compression scheduler processes high-priority volumes first, optimizing the overall efficiency of compression operations.
3Quantity of substance
If compression is applied to volumes with low compression potential, then processing resources are consumed, but minimal storage space savings are achieved
Solution Approach 1:
The system performs preliminary analysis of storage volume characteristics and predicts compression savings before initiating compression. This preliminary action identifies volumes with high compression potential, ensuring that processing resources are concentrated on volumes that will deliver significant storage space savings.
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.