Predictive ML Model for Cloud Change Request Outage Risk
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Solution Overview
Problem
Conventional systems in cloud computing environments lack effective methods to predict and mitigate outage risks associated with change requests, often resulting in downtime and potential violations of service level agreements (SLAs), which can lead to penalties and refunds.
Innovation Solution
A computer-implemented method using a predictive machine learning model that analyzes historical data and features to estimate outage risks and suggests recommendations for mitigating these risks, incorporating a risk estimator, action recommender, and environment predictor to manage change request queues and prioritize risk mitigation actions.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If conventional systems implement change requests without predictive risk analysis, then operational speed and ease of deployment are improved, but system reliability and SLA compliance deteriorate due to unexpected outages
Solution Approach 1:
The system performs preliminary risk assessment and predictive analysis before change requests are deployed. The predictive machine learning model analyzes historical data and current system state to forecast potential outages, allowing operators to take preventive actions or adjust deployment timing before actual changes occur, thus maintaining both deployment speed and system reliability
Solution Approach 2:
The system implements continuous feedback loops where outage predictions from the machine learning model are fed back into the change request management process. This feedback mechanism allows the system to dynamically adjust deployment decisions based on predicted risks, enabling fast deployment when risks are low and preventing deployments when risks are high, thereby resolving the contradiction between speed and reliability
2Measurement precision
If comprehensive historical data analysis is performed to predict outage risks, then prediction accuracy is improved, but computational complexity and processing time increase
Solution Approach 1:
The system segments the comprehensive historical data into relevant features and dimensions that are most predictive of outages. The machine learning model processes segmented data subsets in parallel, analyzing different aspects such as system configuration, operational patterns, and historical incident data separately, then combines results to achieve high prediction accuracy without overwhelming computational complexity
Solution Approach 2:
The system dynamically adjusts analysis parameters such as the time window for historical data consideration, the depth of feature analysis, and the complexity of predictive models based on the specific change request context. This allows the system to optimize between prediction accuracy and computational resources, using more sophisticated analysis only when necessary for high-risk changes
Data Source
AI summary
A method, system, and computer program product that is configured to: receive at least one change request (CR) for a modification in a cloud environment; predict an outage risk for the at least one CR in the cloud environment using a predictive machine learning model which predicts based on historical data and historical features; and suggest at least one recommendation to mitigate the outage risk for the at least one CR in the cloud environment. In particular, embodiments are based on feature objects (or feature sets) (f, e), which are separation of factors pertaining to the CR and to a predicted environment at a currently scheduled CR execution time, as well as dependencies on the features of other CRs in the queue.