Data Cluster Management with Strict Hierarchy
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Solution Overview
Problem
Current computer systems face challenges in managing data clusters due to the absence of a strict hierarchy among objects, leading to resource-intensive locking mechanisms and inefficient garbage collection, especially when dealing with large virtual address ranges and complex object references across multiple pages, which affects system performance.
Innovation Solution
A method and system for managing data clusters by generating data clusters with a strict hierarchy, extracting and storing object references in separate containers, and providing memory access based on these references, allowing for efficient internal and external object reference management and garbage collection.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If objects are spread over multiple pages without strict hierarchy, then virtual memory can accommodate large address ranges, but page faults increase and system resources are consumed
Solution Approach 1:
The system segments objects into hierarchical levels (root objects, container objects, leaf objects) and organizes them across multiple pages with a strict hierarchy. This segmentation allows virtual memory to accommodate large address ranges while reducing random page faults by grouping related objects hierarchically, thus improving system performance.
Solution Approach 2:
The system performs preliminary organization of objects into a strict hierarchical structure before memory access occurs. By pre-establishing parent-child relationships and organizing objects into containers and leaves, the system reduces the likelihood of page faults during runtime, improving productivity without sacrificing virtual address range adaptability.
2Reliability
If locking mechanisms are used to manage concurrent access, then data consistency is maintained, but resource consumption increases
Solution Approach 1:
The system segments the object model into a strict hierarchy with root objects, container objects, and leaf objects. This segmentation enables fine-grained locking where only specific portions of the hierarchy need to be locked during concurrent access, maintaining data consistency while reducing the scope of locks and thus resource consumption compared to coarse-grained locking approaches.
Solution Approach 2:
The system applies locking mechanisms locally to specific hierarchical segments rather than globally. By allowing concurrent access to different branches of the object hierarchy simultaneously, the system maintains data consistency within each locked segment while minimizing resource consumption through reduced lock contention.
3Adaptability or versatility
If objects are spread across multiple pages, then memory flexibility is improved, but garbage collection becomes difficult
Solution Approach 1:
The system segments the object model into a strict hierarchy with clear parent-child relationships. This hierarchical segmentation enables efficient garbage collection by allowing the system to traverse from root objects through container objects to leaf objects in a systematic manner, determining reachability and identifying garbage objects even when they are spread across multiple pages, thus maintaining memory flexibility while reducing garbage collection complexity.
Solution Approach 2:
The system performs preliminary organization of objects into a strict hierarchical structure with explicit parent-child relationships. This preliminary action establishes clear traversal paths for garbage collection, making it easier to determine object reachability and identify garbage objects across multiple pages, thereby reducing garbage collection complexity while maintaining memory flexibility.
4Adaptability or versatility
If no strict hierarchy is enforced, then object reference flexibility is improved, but memory access efficiency decreases
Solution Approach 1:
The system segments objects into a strict hierarchy with root objects, container objects, and leaf objects. This hierarchical segmentation provides both flexibility through multiple reference paths and efficiency by organizing related objects in predictable memory locations, allowing the system to maintain object reference flexibility while improving memory access efficiency through localized access patterns.
Solution Approach 2:
The system applies local quality by organizing objects with related access patterns into the same hierarchical containers. This local organization improves memory access efficiency by reducing page faults for frequently accessed related objects while maintaining the flexibility of object references through the hierarchical structure that allows multiple access paths.
Data Source
AI summary
A method and system for managing data clusters is provided. A first data cluster is generated having a first data object and a second data object. The first data cluster has a strict hierarchy between the first data object and the second data object. A first object reference from the first data object to the second data object is extracted. The first object reference is stored in a first reference container. A second object reference from the first data object to a third data object is extracted. The third data object is stored in a second data cluster. The second object reference is stored in a second reference container. The second object reference is stored in a second reference container. A memory access is provided to the first data cluster based on the first object reference and the second object reference.