Continuous Scheduler for Data Center Test Automation
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Solution Overview
Problem
Existing data center management systems face challenges in efficiently managing and monitoring large numbers of assets due to interconnected dependencies and the need for constant revisions in test execution plans, leading to delays and increased costs.
Innovation Solution
A data-driven autonomous data center test automation orchestration system that generates a continuous test plan using historical test data, applies machine-readable specifications to discover and reserve resources, and automatically adjusts execution plans in response to defects and real-time availability.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If traditional test execution plans are used with static scheduling, then initial planning is simple, but the plans require constant revisions due to interconnected dependencies and changing constraints, leading to delays and increased costs
Solution Approach 1:
The patent implements dynamic scheduling by continuously monitoring test execution status, dependencies, and constraints, then automatically adjusting the execution plan in real-time. The scheduler transitions from static to dynamic operation, allowing the test execution plan to adapt automatically to changing conditions without requiring manual revisions, thus resolving the contradiction between adaptability and time loss.
Solution Approach 2:
The system incorporates continuous feedback loops that monitor test execution progress, detect dependencies, and trigger automatic plan adjustments. This feedback mechanism enables the scheduler to respond to changing constraints and execution status, maintaining adaptability while minimizing the time required for revisions through automated decision-making.
2Reliability
If manual revision of test execution plans is performed to accommodate uncertainties, then plan accuracy can be maintained, but productivity decreases due to repeated revisions and delays
Solution Approach 1:
The scheduling system performs self-service by automatically analyzing test dependencies, monitoring execution status, and generating revised execution plans without human intervention. The system maintains reliability through automated constraint satisfaction and conflict resolution, while significantly improving productivity by eliminating manual revision processes and reducing the time required for plan adjustments.
Solution Approach 2:
The system dynamically changes execution parameters such as test sequence, resource allocation, and timing based on real-time conditions. By automatically adjusting these parameters in response to execution status and constraints, the system maintains reliable test execution plans while avoiding the productivity loss associated with manual revisions.
3Adaptability or versatility
If continuous monitoring and adjustment of test plans is implemented, then adaptability to changing constraints improves, but device complexity increases
Solution Approach 1:
The scheduling system achieves multi-functionality by integrating multiple capabilities into a single unified scheduler: dependency analysis, constraint monitoring, plan generation, and automatic adjustment. This universal approach handles diverse test execution scenarios and constraint types through a single system, improving adaptability while managing complexity through consolidation rather than proliferation of separate components.
Solution Approach 2:
The system introduces an intermediary scheduling layer between test definition and execution, which absorbs and mediates the complexity of continuous monitoring and adjustment. This intermediary component handles the computational burden of constraint satisfaction and plan optimization, allowing the overall system to achieve high adaptability without proportionally increasing observable complexity.
Data Source
AI summary
A system, method, and computer-readable medium for performing a data center management and monitoring operation. The data center management and monitoring operation includes: receiving a plurality of system under test (SUT) test plans, each SUT test plan comprising a plurality of SUT test cases; analyzing the plurality of SUT test cases; generating a continuous schedule for the SUT test plan; and, continuously orchestrating the SUT test plan based upon the continuous schedule for the SUT test plan.