File Tiering Analytics for Distributed Storage Complexity
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Solution Overview
Problem
Existing systems for managing enterprise data storage are complex and cumbersome, leading to incomplete analysis of file system usage characteristics and inefficient resource utilization, with potential for data anomalies due to incomplete catalogs.
Innovation Solution
A cloud-hosted data analytics system for file servers that centralizes data from multiple locations, providing near-real-time analytics and alerts, and utilizes metadata and event-based analytics to optimize tiering and recall operations across distributed file systems.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Quantity of substance
If existing systems store enterprise data with complex structures, then data storage capacity is improved, but system complexity and ease of operation deteriorate
Solution Approach 1:
The patent segments the file system into multiple tiers (hot, warm, cold storage) with different performance characteristics and cost structures. This segmentation allows the system to handle large quantities of data while presenting a simplified, unified interface to users, resolving the contradiction between storage capacity and system complexity.
Solution Approach 2:
The patent introduces an analytics system as an intermediary layer between the complex multi-tier storage infrastructure and the users. This intermediary automatically performs tiering decisions, anomaly detection, and usage analysis, hiding the underlying complexity while enabling efficient data management at scale.
2Measurement precision
If analytics systems provide comprehensive file system analysis, then measurement precision is improved, but system complexity and ease of operation worsen
Solution Approach 1:
The analytics system implements self-service capabilities by automatically collecting metadata, analyzing usage patterns, detecting anomalies, and making tiering decisions without requiring manual configuration or intervention. This automation maintains high measurement precision while reducing the operational complexity for users.
Solution Approach 2:
The system continuously monitors file access patterns and storage usage, using this feedback to dynamically adjust tiering decisions and identify anomalies. This closed-loop feedback mechanism improves measurement precision while the automated nature of the feedback processing reduces operational complexity.
3Ease of operation
If tiering operations are performed manually, then ease of operation is maintained, but productivity and resource utilization deteriorate
Solution Approach 1:
The tiering system operates autonomously by automatically analyzing file usage patterns, determining optimal storage tiers, and executing tiering operations without manual intervention. This self-service approach maintains operational simplicity for users while dramatically improving productivity through continuous, automated optimization.
Solution Approach 2:
The system performs preliminary analysis of file access patterns and usage characteristics before executing tiering operations. This preliminary action ensures that tiering decisions are optimized based on actual usage data, improving productivity while the automated nature maintains ease of operation.
4Adaptability or versatility
If distributed file systems are used to improve scalability, then adaptability is improved, but system complexity and ease of operation worsen
Solution Approach 1:
The analytics system provides universal functionality across the distributed file system, handling metadata collection, usage analysis, anomaly detection, and tiering decisions in a single unified platform. This universal approach enables scalability across multiple distributed nodes while hiding the underlying complexity through standardized interfaces and centralized management.
Data Source
AI summary
Data analytics systems are described herein which may provide requests for file tiering to one or more file servers. The data analytics systems may receive metadata and/or event data from one or more file servers and may utilize the metadata and/or event data to select files for tiering. In some examples, files may be selected using a sliding window methodology. In some examples, files may be selected in part based on user behavior with the files in the file system. In some examples, file analytics systems may send requests to retry tiering operations which failed. The retry requests may be sent in a manner that is based on the error which caused the failure.