Automated Data Offloading Using Machine Learning
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Conventional database management approaches are resource-intensive, time-intensive, and error-prone, leading to increased architectural complexity and inefficiencies in data offloading, particularly due to the accumulation of inactive data and the need for manual tuning and scripting.
Innovation Solution
The implementation of automated data offloading methods using data bucketing and machine learning techniques, which involve obtaining operations data, determining optimal times for data offloading, generating data offloading protocols, and executing them automatically, thereby reducing resource usage and improving performance.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of operation
If manual tuning and scripting are used for data offloading, then data management can be performed, but the process becomes resource-intensive and time-intensive
Solution Approach 1:
The system automatically performs data offloading by detecting inactive data, determining optimal offloading methods, and executing protocols without manual intervention. The automated data offloading system monitors database storage objects, identifies candidates for offloading based on inactivity criteria, and autonomously executes data movement operations, eliminating the need for manual tuning and scripting while improving efficiency
Solution Approach 2:
The system proactively identifies data that is likely to become inactive and performs offloading before it actually becomes inactive. By analyzing patterns and predicting future inactivity, the system prepares and executes data offloading in advance, preventing resource contention before it occurs and improving overall system performance
2Quantity of substance
If manual scripts are used to purge and archive data, then data volume can be reduced, but the process becomes error-prone and increases architectural complexity
Solution Approach 1:
The automated data offloading system provides a unified, multi-functional platform that handles data identification, method selection, protocol generation, and execution within a single integrated architecture. This universal system replaces multiple separate manual processes (purging, archiving, copying) with one cohesive mechanism, reducing architectural complexity while effectively managing data volume
Solution Approach 2:
The system continuously monitors database operations and storage object states, using this feedback to dynamically adjust offloading decisions. By implementing closed-loop control where the system observes actual data access patterns and refines its predictions and actions accordingly, manual intervention and complex error-handling scripts become unnecessary, simplifying the architecture while maintaining accurate data volume reduction
3Quantity of substance
If additional storage hardware is acquired to handle data growth, then data capacity increases, but resource costs and architectural complexity increase
Solution Approach 1:
The system automatically identifies and offloads inactive data from primary database storage to archival storage or data lakes. By systematically discarding data from active storage that is no longer needed and recovering space in the primary database, the system maintains data capacity without requiring continuous acquisition of additional expensive storage hardware, thereby avoiding increased architectural complexity
4Duration of action of stationary object
If data is retained longer for regulatory compliance, then compliance requirements are met, but data management costs and resource usage increase
Solution Approach 1:
The system applies different storage quality levels to different data based on their access patterns and importance. Active, frequently accessed data remains in high-performance storage, while inactive data compliant with retention requirements is moved to lower-cost archival storage. This local differentiation of storage quality allows the system to meet long-term retention requirements while significantly reducing the resource usage associated with managing all data uniformly in high-performance storage
Data Source
AI summary
Methods, apparatus, and processor-readable storage media for automatically detecting data offloading methods using data bucketing and machine learning techniques are provided herein. An example computer-implemented method includes obtaining operations data and configuration data for one or more storage objects in a database; determining one or more times at which data offloading is to be carried out for at least one of the storage objects in the database, wherein determining the one or more times includes processing at least a portion of the operations data using one or more machine learning techniques; generating at least one data offloading protocol, comprising one or more data offloading methods, by processing at least a portion of the configuration data; and automatically executing, in accordance with the one or more determined times, the at least one generated data offloading protocol for at least a portion of the one or more storage objects in the database.