Storage Slice Pool Consumption Estimation via Historical Data
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Solution Overview
Problem
Conventional data storage systems rely solely on actual consumption amounts to manage storage pools, which can lead to complete pool filling and subsequent failure to handle further input/output requests, as they do not account for future usage patterns.
Innovation Solution
Implementing a method to predict slice consumption based on historical data to estimate when a storage pool will reach specific utilization milestones, allowing for proactive alerts and automatic deletion of snapshot data to prevent pool overflow.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If conventional storage management relies solely on actual consumption amounts, then the system operates with simple monitoring, but the storage pool becomes completely filled and I/O requests fail
Solution Approach 1:
The system performs preliminary actions by predicting future storage consumption based on historical data before the storage pool becomes full. It calculates projected consumption rates and identifies when thresholds will be reached, enabling proactive remedial activities such as alerting users or automatically deleting snapshot data before pool exhaustion occurs, thus maintaining I/O reliability without reactive complexity
Solution Approach 2:
The system implements feedback by continuously monitoring actual storage consumption, comparing it against predicted consumption based on historical patterns, and using this feedback loop to adjust predictions and trigger remedial actions. The feedback mechanism compares actual usage with projected usage to determine when intervention is needed, resolving the contradiction between simple monitoring and reliable operation
2Productivity
If the system proactively predicts storage consumption and performs remedial activities, then the storage pool remains operational, but the system requires complex prediction and monitoring mechanisms
Solution Approach 1:
The storage system performs self-service by automatically monitoring its own consumption patterns, predicting future usage based on historical data, and executing remedial activities without external intervention. The system serves itself by generating predictions from its own operational history and autonomously triggering alerts or deletions when thresholds are projected to be reached, maintaining productivity while managing complexity internally
Solution Approach 2:
The system replaces mechanical manual monitoring and intervention with an automated prediction mechanism that uses historical consumption data to forecast future usage. Instead of manual tracking and reactive responses, the patent substitutes an automated system that calculates consumption rates and triggers actions based on projected thresholds, ensuring operational continuity while managing complexity through automation
3Ease of operation
If automatic deletion of snapshot data is triggered only at specified usage limits, then the system operates with simple rules, but the storage pool may become completely filled leaving no space for new data
Solution Approach 1:
The system applies preliminary action by predicting when storage thresholds will be reached based on historical consumption patterns before actual limits are hit. It calculates projected consumption rates and triggers remedial activities in advance, ensuring that snapshot data is deleted proactively while maintaining simple automated rules, thus preserving available storage space without complex manual intervention
Solution Approach 2:
The system uses feedback by continuously comparing actual storage consumption against predicted consumption based on historical data. This feedback mechanism allows the system to adjust its predictions and trigger deletions at optimal moments, maintaining simple automated rules while ensuring sufficient storage space remains available by acting on projected rather than reactive thresholds
Data Source
AI summary
A technique is directed to managing a pool of slices provided by data storage equipment. The technique involves monitoring usage of the pool of slices provided by the data storage equipment, and generating an estimate based on the monitored usage of the pool of slices. The estimate identifies when a slice usage level of the pool of slices will reach a predefined usage level (e.g., the number of days remaining before the pool will become completely full, 90% full, 80% full, etc.). The technique further involves, in response to the estimate reaching a predefined threshold (e.g., when the number of days remaining is 5 days, 4 days, 3 days, etc.), performing a remedial activity to prevent full consumption of the pool of slices.