Knowledge Graph Asset Tracking in Cloud Environments
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Solution Overview
Problem
Cloud computing environments face challenges in managing and tracking ephemeral assets, leading to inventory inaccuracies and poor visibility, which complicates resource allocation and cost management across multi-cloud deployments.
Innovation Solution
A knowledge graph model is created to track ephemeral assets by establishing nodes and relationships, with periodic updates and comparisons of adjacency lists to determine changes over time, enabling real-time asset management and cost optimization through machine learning predictions.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If traditional cloud asset management methods are used, then implementation is simple, but visibility and tracking accuracy of ephemeral assets deteriorate
Solution Approach 1:
The patent segments cloud asset management into distinct components: a knowledge graph module for structural representation, an adjacency list module for relationship tracking, and a change detection module for monitoring. This segmentation allows each component to specialize in specific tracking tasks, improving overall measurement precision while managing system complexity through modular design.
Solution Approach 2:
The patent introduces a temporal dimension by maintaining adjacency lists at multiple time points and comparing them to detect changes. This dimensional approach transforms static asset inventory into dynamic tracking, enabling accurate detection of ephemeral asset lifecycle changes without proportionally increasing system complexity.
2Loss of information
If comprehensive tracking of all cloud assets is implemented, then visibility improves, but resource consumption increases
Solution Approach 1:
The patent extracts and tracks only the critical relationship changes between assets using adjacency lists, rather than monitoring all asset properties continuously. This extraction approach maintains comprehensive visibility of asset relationships while reducing resource consumption by focusing computational efforts only on changing states.
Solution Approach 2:
The system performs periodic comparisons of adjacency lists at different time points rather than continuous monitoring. This periodic action approach ensures comprehensive tracking of asset changes while optimizing resource consumption by executing tracking operations at intervals rather than continuously.
3Reliability
If real-time tracking of ephemeral assets is achieved, then inventory accuracy improves, but system complexity increases
Solution Approach 1:
The patent creates simplified copies of asset relationship structures through adjacency lists that mirror the knowledge graph. These copies enable efficient real-time tracking and comparison operations without directly manipulating the complex knowledge graph structure, thereby maintaining inventory accuracy while reducing management complexity.
Solution Approach 2:
The adjacency list serves as an intermediary data structure between the knowledge graph and the change detection mechanism. This intermediary simplifies the comparison operation by providing a standardized format for tracking relationships, improving inventory accuracy while reducing the complexity of real-time tracking operations.
Data Source
AI summary
Computer-readable media, methods, and systems are disclosed for tracking ephemeral assets in a cloud environment by creating a knowledge graph model comprising a plurality of nodes and a plurality of relationships between the plurality of nodes. The media, method, and system further include determining properties of the knowledge graph model for a first node at a first time and creating a first adjacency list for the first node at the first time. Additionally, properties of the knowledge graph model are determined for the first node at a second time and a second adjacency list is created for the first node at the second time. By comparing the first adjacency list to the second adjacency list, at least one change that occurred between the first time and the second time can be determined.