ML-Based IaC Pre-Deployment Analysis
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Solution Overview
Problem
Infrastructure as code (IaC) deployments often face issues due to inconsistencies, omissions, or problems in deployment code, leading to unsuccessful deployments, user dissatisfaction, and significant debugging and support costs.
Innovation Solution
Implementing pre-deployment analysis using a machine-learning model (MLM) trained on previously generated deployment codes and their success/failure data to predict the likelihood of success for new deployments, thereby identifying and preventing potential issues.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If traditional deployment methods are used without pre-deployment analysis, then deployment speed is maintained, but deployment reliability deteriorates due to undetected issues in deployment code
Solution Approach 1:
The patent applies preliminary action by performing pre-deployment analysis using a machine learning model before actual deployment executes. The MLM analyzes deployment code to predict potential failures and generate warnings in advance, allowing issues to be identified and corrected before they cause deployment failures, thus improving reliability without significantly impacting deployment time
2Ease of repair
If manual debugging and support processes are used for deployment failures, then detailed problem analysis is possible, but support costs and time consumption increase significantly
Solution Approach 1:
The system performs preliminary analysis of deployment code before deployment failures occur. The machine learning model predicts potential issues and generates detailed warning information in advance, so when deployment problems do occur, debugging time is significantly reduced because the issues have already been identified and can be quickly addressed
Solution Approach 2:
The patent implements feedback by using results from previous deployment successes and failures to continuously train and improve the machine learning model. The MLM learns from historical deployment data to become more accurate in predicting future deployment issues, creating a feedback loop that continuously improves debugging efficiency and reduces support costs
Data Source
AI summary
Deployment code that identifies a desired deployment of a computer application onto a computing system comprising one or more compute instances is accessed. The computer application includes a plurality of computing components to be deployed on the one or more compute instances in accordance with the deployment code. Contents of the deployment code are input into a machine learning model (MLM) that has been trained using: a plurality of previously generated deployment codes that identified corresponding desired deployments of corresponding computer applications, and information that identifies successes or failures associated with attempted deployments of the previously generated deployment codes. An output is received from the MLM. Based on the output from the MLM, information indicative of a likelihood of success of deploying the deployment code is sent to a destination.