Dynamic Storage Quota Management for Distributed File Systems
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Solution Overview
Problem
Current distributed file systems for big data platforms, such as HDFS, require manual and inflexible quota management, leading to inefficiencies and productivity losses due to the inability to accurately predict and adapt to changing storage needs across multiple directories and teams.
Innovation Solution
A system and method that uses machine learning to dynamically predict future storage needs and adjust quotas in real-time, balancing the storage requirements of individual directories with the overall cluster resources, preventing data loss and optimizing resource utilization.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If manual quota limits are enforced in distributed file systems, then storage resource allocation control is improved, but system flexibility and adaptability to changing storage needs deteriorate
Solution Approach 1:
The patent implements dynamic quota management by continuously monitoring storage usage patterns and automatically adjusting quotas in real-time based on actual needs. The system transitions from static pre-defined limits to dynamic adaptive limits that respond to changing storage demands, resolving the contradiction between control reliability and adaptability.
Solution Approach 2:
The system enables self-service quota management through automated monitoring and adjustment mechanisms. The distributed file system autonomously tracks storage consumption, predicts future needs, and modifies quotas without manual intervention, maintaining control while adapting to changing requirements.
2Device complexity
If pre-defined storage allocation limits are enforced, then storage resource management is simplified, but productivity and efficiency deteriorate due to inability to predict and adapt to changing needs
Solution Approach 1:
The patent implements feedback mechanisms that continuously monitor storage usage patterns and feed this information back to the quota management system. This enables automated adjustment of quotas based on actual consumption patterns, maintaining simplicity while improving productivity through adaptive resource allocation.
Solution Approach 2:
The system performs preliminary actions by proactively predicting future storage needs based on historical patterns and adjusting quotas before resources are exhausted. This prevents productivity disruptions while maintaining manageable system complexity through automated forecasting.
3Reliability
If manual quota adjustment is performed, then control over storage allocation is maintained, but time consumption and operational overhead increase
Solution Approach 1:
The system implements self-service automation where the distributed file system autonomously monitors storage usage, predicts future needs, and adjusts quotas without manual intervention. This maintains reliable quota control while eliminating time consumption associated with manual quota management.
Solution Approach 2:
The patent replaces manual mechanical quota adjustment processes with automated computational systems that use algorithms to monitor, predict, and adjust quotas. This substitution eliminates human time consumption while maintaining control reliability through systematic automated management.
Data Source
AI summary
There is provided a computer-implemented method for automatically managing storage resources of a distributed file system comprising: obtaining actual past storage usage data of a first directory from a plurality of directories of the distributed file system to a current time; detecting, a space quota limit for the first directory and associated with a pre-defined expected future time; determining from the actual past storage usage data, projected storage usage data of the first directory over a future time period; obtaining an aggregated correction coefficient providing an indication of aggregated projected storage usage needs of remaining other directories relative to the first directory; in response to determining an expected value of the projected storage usage data at the expected future time is inconsistent with the space quota limit, adjusting the space quota limit to a new quota limit based on the expected value weighted by the aggregated correction coefficient.