Cloud Resource Testing Schedules Using Compatibility Graph Optimization
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Solution Overview
Problem
Conventional cloud computing resource testing methods face challenges in efficiently generating testing schedules due to out-of-memory errors and excessive processing time when enumerating all possible node configurations, especially in diverse cloud computing environments with varying server/node properties.
Innovation Solution
An automated cloud computing resource testing process that generates a testing schedule using a graph structure based on compatibility constraints and prevalence metrics, employing constrained optimization techniques like simulated annealing and branch and bound optimization to iteratively modify cliques, thereby optimizing the testing schedule without enumerating all configurations.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If all possible node configurations are enumerated and stored in memory for testing, then complete testing coverage is achieved, but out-of-memory errors occur and processing time becomes excessive
Solution Approach 1:
The patent segments the testing process by dividing the fleet of cloud computing devices into multiple groups or batches. Instead of enumerating all possible node configurations at once, the system processes configurations in segmented batches, testing agents on subsets of the fleet simultaneously. This segmentation reduces peak memory usage while maintaining comprehensive testing coverage across all device types.
Solution Approach 2:
The system dynamically adjusts the testing schedule and configuration selection based on available resources and testing progress. Rather than using a static enumeration of all configurations, the system dynamically generates and prioritizes configurations to test based on prevalence metrics, compatibility constraints, and current resource availability, optimizing memory usage while ensuring critical configurations are tested.
2Reliability
If all possible node configurations are enumerated and stored in memory for testing, then complete testing coverage is achieved, but processing time becomes excessive
Solution Approach 1:
The system performs preliminary actions by pre-computing and storing only the essential compatibility relationships and prevalence metrics in a compact data structure before testing begins. This preliminary setup avoids the need to enumerate all configurations during the actual testing phase, significantly reducing processing time while ensuring complete coverage through the pre-established compatibility framework.
Solution Approach 2:
The system changes parameters by using prevalence metrics and compatibility constraints to prioritize and select which configurations to test, rather than testing all configurations uniformly. This parameter-based selection approach reduces the effective number of configurations that need processing while maintaining comprehensive coverage of critical scenarios.
3Reliability
If conventional enumeration methods are used to generate testing schedules, then all configurations can be tested, but the system becomes inefficient in diverse cloud computing environments
Solution Approach 1:
The system uses prevalence metrics as parameters to weight and prioritize different node configurations based on their actual occurrence in the fleet. Instead of treating all configurations equally, the system adjusts testing priorities based on these parameters, efficiently allocating testing resources to the most prevalent and critical configurations while maintaining completeness through the compatibility constraint framework.
Solution Approach 2:
The patent replaces the mechanical enumeration approach with a graph-based computational system. By representing compatibility relationships as a graph data structure and using algorithmic optimization to generate test schedules, the system achieves much higher efficiency in diverse environments compared to traditional enumeration methods, while ensuring all valid configurations are covered.
Data Source
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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.