Garbage Collection Prediction Using Temporal Graphs
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Current data management systems rely on multiple point solutions for managing the lifecycle of application data, leading to complex and expensive infrastructures with inefficient data replication and garbage collection processes, particularly in virtualized environments where data changes slowly over time.
Innovation Solution
The implementation of a Data Management Virtualization system that uses synthetic near neighbors and history tree-based statistics for efficient data replication and garbage collection predictions, leveraging deduplication and compression algorithms to reduce data duplication and optimize storage and network bandwidth by tracking data changes and using snapshot capabilities for efficient data movement.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If multiple point solutions are deployed for managing data lifecycle, then data protection coverage is improved, but infrastructure complexity and cost increase
Solution Approach 1:
The patent combines multiple separate data management functions (backup, replication, archiving, disaster recovery) into a single unified data management system. This consolidation maintains comprehensive data protection coverage while reducing infrastructure complexity by eliminating the need for multiple separate point solutions and their associated management overhead.
Solution Approach 2:
The unified data management system performs multiple functions including backup, replication, archiving, and disaster recovery through a single platform. This multi-functional approach allows the system to provide comprehensive data protection coverage while avoiding the complexity of deploying and managing multiple specialized systems.
2Reliability
If multiple copies of data are created and moved to individual storage repositories, then data protection is improved, but storage costs and network bandwidth consumption increase
Solution Approach 1:
The patent uses deduplication technology to create efficient copies of data. Instead of storing complete duplicate copies across multiple repositories, the system identifies and stores only unique data blocks, eliminating redundant storage while maintaining data protection through multiple copies where needed.
Solution Approach 2:
The system changes the storage parameters by implementing deduplication and compression algorithms. This transforms the storage approach from storing complete data copies to storing unique data blocks with references, significantly reducing the quantity of stored data while maintaining data protection capabilities.
3Stability of the object's composition
If traditional garbage collection processes are used in virtualized environments, then data retention is maintained, but processing efficiency decreases due to slow data changes
Solution Approach 1:
The patent implements an improved garbage collection mechanism that uses feedback from data change tracking. By monitoring data modification patterns in virtualized environments and using this feedback to optimize garbage collection timing and scope, the system maintains data retention requirements while significantly improving processing efficiency compared to traditional approaches.
Data Source
AI summary
Described herein are systems and methods for garbage collection prediction. A temporal graph is received, the temporal graph including nodes, the nodes including hash references to objects. An accumulated difference count is updated when a node is added to the temporal graph, the accumulated difference count including a number of hash differences between a parent node and its children nodes in the temporal graph. A divested difference count is updated when a node is removed from the temporal graph, the divested difference count including a number of hash differences referenced by the removed node but not by either a parent node of the removed node or any child nodes of the removed node. The outcome of the garbage collection is predicted based on at least one of the accumulated difference count and the divested difference count.