Cloud Resource Testing Schedules Using Compatibility Graphs
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Solution Overview
Problem
Conventional cloud computing resource testing methods face inefficiencies due to out-of-memory errors and excessive processing time when enumerating all possible configurations, failing to consider the diversity of server/node properties across a fleet of devices and dynamically emphasize the prevalence of certain node properties, and the prevalence of certain node properties, and the need to optimize testing schedules to ensure compatibility and reliability of cloud computing environments, compatibility constraints, and prevalence metrics.
Innovation Solution
An automated cloud computing resource testing process that generates a testing schedule using a graph structure and constrained optimization, iteratively modifying cliques to optimize compatibility constraints and prevalence metrics, avoiding out-of-memory errors and reducing processing time.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If all possible configurations are enumerated to ensure comprehensive testing coverage, then testing completeness is improved, but memory consumption and processing time increase excessively
Solution Approach 1:
The patent segments the configuration space into a graph structure where nodes represent individual configurations and edges represent compatibility relationships. This segmentation allows the system to explore the configuration space systematically without enumerating all possibilities, thereby reducing memory consumption while maintaining comprehensive testing coverage through smart traversal algorithms.
Solution Approach 2:
The patent introduces an intermediary graph structure that mediates between the comprehensive testing requirement and memory constraints. The graph acts as a mediator that organizes configuration relationships, allowing the system to navigate and explore configurations efficiently without storing all possible configurations in memory simultaneously.
2Reliability
If all possible configurations are enumerated to ensure comprehensive testing coverage, then testing completeness is improved, but processing time increases excessively
Solution Approach 1:
The patent segments the configuration space into a graph structure with nodes representing configurations and edges representing compatibility relationships. This segmentation enables systematic exploration of the configuration space through graph traversal algorithms, significantly reducing processing time compared to exhaustive enumeration while maintaining comprehensive testing coverage.
Solution Approach 2:
The patent performs preliminary actions by pre-building the graph structure that represents configuration compatibility relationships. This preliminary graph construction allows subsequent testing schedule generation to proceed much faster through efficient graph traversal, avoiding the need to re-evaluate all configuration combinations during the actual testing scheduling process.
3Ease of manufacture
If conventional testing methods are used without optimization, then implementation simplicity is maintained, but testing efficiency and resource utilization deteriorate
Solution Approach 1:
The patent implements self-service through automated generation of testing schedules based on the graph structure and compatibility constraints. The system automatically determines optimal test configurations and schedules without requiring manual intervention, thereby improving testing efficiency while maintaining relatively simple implementation through standardized graph-based algorithms.
Data Source
AI summary
A method, computer program product, and computing system for processing a plurality of compatibility constraints for a cloud computing environment. A plurality of prevalence metrics for the cloud computing environment are processed. A graph structure mapping compatibility relationships between cloud computing resources is generated based upon, at least in part, the plurality of compatibility constraints. A constrained optimization process is defined for the graph structure. A testing schedule for the cloud computing resources is generated using the graph structure and the constrained optimization process.