Automated Container Tuning via Iterative ML Optimization
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Solution Overview
Problem
The manual and time-consuming process of application 'tuning' in containerized environments leads to inefficient resource utilization and suboptimal configurations, resulting in significant waste and potential service disruptions.
Innovation Solution
An automated method using machine learning techniques to optimize containerized application deployments by iteratively updating optimization models based on trial results and stopping criteria, allowing for intelligent parameter value suggestions and efficient resource allocation.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of operation
If manual trial-and-error methods are used for application tuning, then engineers can control the tuning process, but the process becomes time-consuming and resource-intensive
Solution Approach 1:
The system enables self-service tuning by automatically executing trials, collecting metrics, updating optimization models, and selecting parameter values without requiring manual intervention from engineers for each tuning iteration
Solution Approach 2:
The system implements feedback loops where trial results and metrics are continuously fed back to update the optimization model, which then generates improved parameter values for the next trial, creating an automated iterative improvement process
2Ease of operation
If manual tuning is performed, then configuration decisions can be made with human judgment, but resource utilization remains inefficient
Solution Approach 1:
The patent replaces manual mechanical tuning processes with an automated computational system that uses optimization models and machine learning algorithms to determine optimal parameter values, eliminating inefficient manual resource allocation
Solution Approach 2:
The system automatically changes configuration parameters by selecting optimal values from multiple possibilities based on the optimization model, enabling efficient resource utilization without manual intervention
3Difficulty of detecting and measuring
If reactive monitoring is used to detect issues after deployment, then problems can be identified, but by the time issues surface, costs have escalated or service disruptions occur
Solution Approach 1:
The system performs preliminary tuning and optimization before deployment by conducting trials in advance, ensuring that configurations are optimized beforehand rather than reacting to problems after they occur
Solution Approach 2:
The system continuously monitors trial results and metrics, using this feedback to update optimization models and prevent future issues before they impact production services
Data Source
AI summary
A system, method, and server for optimizing deployment of a containerized application. The system includes a machine and a server configured to receive optimization criteria related to the containerized application, the optimization criteria including affecting parameters, effected metrics, and stopping criteria. The server is further configured to transmit at least one value of the affecting parameter to the machine, receive results of a trial of the containerized application performed by the machine according to the transmitted at least one value, the results of the trial including an empirical value of the effected metrics, update an optimization model based on the trial results, compare the results of the trial and the updated optimization model to the one or more stopping criteria, and transmit an optimized one of the at least one value of the affecting parameters to the machine for deployment of the containerized application.