Storage System Decay Policy for Data Lifecycle Management
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Solution Overview
Problem
Existing data storage systems waste resources by ensuring data availability indefinitely, even when data only needs to be stored for a short period, and often violate compliance regulations by failing to automatically decay data as required.
Innovation Solution
A storage system that generates and enforces a 'decay policy' for data objects, allowing them to automatically decay based on a predetermined lifetime without explicit delete commands, using various layers of the storage stack to implement procedures such as undervolting and reducing redundancy, thereby ensuring compliance with regulations.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If the storage system executes explicit delete commands to remove data objects, then data availability is controlled precisely, but computational resources and operational complexity increase
Solution Approach 1:
The storage system automatically manages data lifecycle by detecting decayed data objects and initiating deletion procedures without requiring explicit user commands. The system monitors data integrity metrics and autonomously removes unrecoverable data, making the deletion process self-service rather than manually triggered.
Solution Approach 2:
The system performs preliminary detection of data decay conditions before actual deletion occurs. By continuously monitoring data integrity metrics and identifying decayed objects in advance, the system prepares for automated deletion, reducing the need for reactive manual intervention and simplifying operational complexity.
2Reliability
If the storage system ensures data availability indefinitely with redundancy and error correction, then data reliability is maximized, but computational and memory resources are wasted
Solution Approach 1:
The storage system dynamically adjusts data protection mechanisms based on the actual state and lifetime requirements of data objects. For decayed data, the system reduces or eliminates redundancy and error correction efforts, while maintaining full protection for active data. This dynamic adaptation prevents continuous waste of computational resources on unrecoverable data.
Solution Approach 2:
The system changes key parameters such as error correction code strength and redundancy level based on data age and decay probability. As data approaches its expected lifetime, the system progressively reduces protection parameters, thereby reducing computational overhead while maintaining reliability for data that is still recoverable.
3Reliability
If the storage system continuously monitors and maintains data integrity, then data reliability is ensured, but system complexity and processing overhead increase
Solution Approach 1:
The system extracts and monitors only the critical integrity metrics necessary to detect data decay, rather than continuously verifying all data attributes. By focusing on key indicators such as error rates and data age, the system maintains effective integrity monitoring while reducing processing overhead and simplifying the monitoring architecture.
4Reliability
If the storage system uses strong error correction codes to prevent data decay, then data reliability is improved, but computational resources and storage overhead increase
Solution Approach 1:
The system applies error correction partially rather than universally. Instead of maintaining full-strength error correction on all data indefinitely, the system applies correction only to data that is still within its useful lifetime and has not yet decayed. This partial application of error correction reduces computational resource consumption while maintaining adequate protection for recoverable data.
Applied Scientific Principles
This section explains which scientific principles are used to turn an abstract innovation direction into a practical engineering solution.
Function Achieved in This Case
This approach saves significant computational and memory resources while ensuring data decay within required timeframes, adhering to compliance regulations by automatically managing data availability and unavailability based on defined lifetimes.
Implementation Method 1
a voltage to provide to multiple memory cells of the storage device, such that an expected time at which raw bit errors of the data object will cause the data object to decay is equal to a time point identified by the lifetime of the data object
Data Source
AI summary
Methods, systems, and apparatus, including computer programs encoded on computer storage media, for enforcing a decay policy for a data object. One of the methods includes receiving a request to store a data object in a storage device; obtaining a user policy identifying a lifetime of the data object; determining, using the lifetime of the data object, a voltage policy for a plurality of memory cells of the storage device, wherein: each of the plurality of memory cells will store one or more bits of the data object; the voltage policy identifies a voltage to provide each memory cell; and an expected time at which raw bit errors of the data object will cause the data object to decay is equal to a time point identified by the lifetime of the data object; and storing the data object in the storage device according to the determined voltage policy.