Multi-Cloud Resource Dependency Graph Analysis
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Solution Overview
Problem
Current multi-cloud infrastructure management lacks an efficient method to detect and resolve resource redundancy and conflict errors across multiple clouds, leading to unnecessary costs and performance issues due to redundant or conflicting resources.
Innovation Solution
A computer-implemented method that consolidates individual resource dependency graphs into a single multi-cloud resource dependency graph, analyzing nodes to identify and address redundancy and conflict errors using defined rule sets, allowing for automatic correction or removal of redundant or conflicting resources.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If multiple cloud services are utilized in a heterogeneous cloud architecture, then service versatility and redundancy are improved, but resource redundancy and conflict errors increase
Solution Approach 1:
The system performs preliminary analysis of resource dependency graphs before deploying new resources. By analyzing the consolidated graph in advance, the system identifies potential redundancy and conflict errors before they occur, allowing preventive correction rather than reactive resolution
Solution Approach 2:
The system continuously monitors and analyzes resource dependency graphs to provide feedback about redundancy and conflicts. This feedback mechanism enables the system to detect when resources are redundant or conflicting and trigger appropriate corrections, maintaining optimal resource utilization across multiple clouds
2Reliability
If resource dependency graphs are consolidated across multiple clouds, then conflict detection capability is improved, but system complexity increases
Solution Approach 1:
The system segments the multi-cloud environment into individual cloud accounts and maintains separate resource dependency graphs for each. These segmented graphs are then consolidated for analysis, allowing the system to manage complexity at the individual cloud level while achieving comprehensive conflict detection at the multi-cloud level
Solution Approach 2:
The system introduces a consolidation layer that acts as an intermediary between individual cloud resource dependency graphs and the overall multi-cloud view. This intermediary structure enables conflict detection across clouds without requiring direct integration of all cloud systems, reducing overall system complexity
3Reliability
If redundant or conflicting resources are deployed, then resource availability is improved, but costs and performance issues increase
Solution Approach 1:
The system continuously analyzes resource dependency graphs to detect redundancy and conflicts, providing feedback that enables corrective action. By identifying issues before deployment, the system prevents wasteful spending on redundant resources while maintaining necessary availability through proper resource distribution
Solution Approach 2:
The system changes the state of resource dependency graphs by analyzing and comparing resource configurations across multiple clouds. This parameter analysis allows the system to identify when resources should be consolidated, removed, or redistributed to eliminate redundancy and conflicts, optimizing both cost and availability
Data Source
AI summary
Detecting resource errors in a heterogeneous computing environment is provided. A plurality of individual resource dependency graphs corresponding to a plurality of computing systems that comprise the heterogeneous computing environment is consolidated to form a consolidated resource dependency graph. An analysis of respective nodes representing respective resources of the heterogeneous computing environment in the consolidated resource dependency graph is performed to identify a resource error caused by a new resource being added to a computing system of the plurality of computing systems based on defined rule sets. It is determined whether the new resource causes an error to sibling resources at a same level under a parent resource in the consolidated resource dependency graph based on the analysis. In response to determining that the new resource does not cause an error to the sibling resources, the new resource is deployed in the computing system of the heterogeneous computing environment.