Cloud Server State Forecasting for Automated Testing
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Solution Overview
Problem
In complex cloud systems, especially during active-active or active-passive Software as a Service (SaaS) deployments, automated testing faces challenges due to code flips, which can cause outages and test failures, necessitating a method to predict and manage future system states for optimal testing timing.
Innovation Solution
A system that determines the operating state of a passive server, calculates the probability of successful completion of deployment and smoke testing, and delays asynchronous workflows until the state meets a predetermined success threshold, ensuring tests are executed after a successful code flip.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If automated testing is executed without manual intervention, then productivity is improved, but reliability deteriorates due to test failures during code flips
Solution Approach 1:
The system performs preliminary actions by monitoring deployment states and predicting future code flips before they occur. Test workflows are delayed until after the predicted flip time, ensuring tests execute against the most current code version and avoiding failures during transition periods.
Solution Approach 2:
The system implements feedback mechanisms by continuously monitoring passive server deployment states and using this information to dynamically adjust test execution timing. The monitoring component provides real-time state information that feeds into the workflow delay logic, creating a closed-loop system that adapts to current deployment conditions.
2Loss of time
If tests are run immediately without checking deployment state, then time is saved, but reliability worsens due to executing tests during outages
Solution Approach 1:
The system performs preliminary monitoring of passive server states to predict upcoming code flips before they occur. By anticipating flip events in advance, the system can proactively delay test workflows to execute after the flip completes, avoiding outages while minimizing overall test delays.
Solution Approach 2:
The system dynamically adjusts test execution timing based on real-time monitoring of passive server deployment states. Rather than using fixed schedules, the delay duration is adaptively determined by current deployment conditions, allowing the system to optimize between immediate execution and strategic delays.
3Adaptability or versatility
If code flips are performed to deploy new versions, then adaptability is improved, but reliability deteriorates due to potential outages during the flip process
Solution Approach 1:
The system performs preliminary monitoring of passive server deployment states to detect when code flips are in progress or about to complete. By identifying flip events before they cause outages, the system can delay test workflows to execute after service stability is restored, maintaining both deployment flexibility and service reliability.
4Reliability
If test workflows are delayed to wait for code flip completion, then reliability is improved, but productivity deteriorates due to extended wait time
Solution Approach 1:
The system performs preliminary monitoring of passive server states to predict code flip completion times in advance. By anticipating when flips will complete, the system can minimize delay duration while ensuring tests execute at the optimal moment, balancing reliability improvement with productivity preservation.
Solution Approach 2:
The system dynamically determines the appropriate delay duration based on real-time monitoring of passive server deployment progress. Rather than using fixed or excessive delays, the system adapts the wait time to match actual flip completion timing, optimizing the balance between ensuring test reliability and maintaining execution speed.
Data Source
AI summary
An embodiment of the invention may include a method, computer program product and computer system for forecasting future states of a multi-active cloud. The method, computer program product and computer system may include a computing device that determines the operating state of the passive server. The operating state of the passive server is one or more of a deploying state and a smoke testing state. The computing device may determine the probability of the operating state successfully completing. The computing device may delay the second asynchronous workflow until the operating state successfully completes based on the probability of the operating state successfully completing exceeding a predetermined value.