Automated Container Task Definition Generation

Solution Overview

Problem

Users face a tedious and time-consuming process when configuring the execution environment for container images in cloud provider networks, requiring them to specify various configuration parameters manually.

Innovation Solution

The system automatically generates a task definition for executing container images by utilizing user-provided information and historical data, reducing the need for manual configuration and optimizing resource allocation.

Engineering Contradictions & Design Principles

VSEngineering Contradiction Analysis

1Manufacturing precision

If users manually specify configuration parameters for container image execution, then the execution environment can be precisely configured, but the time and effort required increases significantly

Engineering Contradiction:

Improveconfiguration precisionVSAvoidconfiguration time

Core Design Contradiction:

Manufacturing precisionVSLoss of time

Solution Approach 1:

The system performs self-service by automatically generating task definitions and configuring execution environments using historical data and machine learning models, eliminating the need for users to manually specify configuration parameters while maintaining precise configuration through automated resource recommendation engines

Inventive Principle:

Principle #25Self-service

Solution Approach 2:

The system performs preliminary action by pre-configuring execution environments based on historical execution data and storing task definitions for future use, allowing users to launch tasks without manual configuration while maintaining precision through pre-computed resource allocations

Inventive Principle:

Principle #10Preliminary action

2Measurement precision

If users manually configure execution environments, then configuration accuracy can be maintained, but user effort and complexity increase

Engineering Contradiction:

Improveconfiguration accuracyVSAvoiduser effort

Core Design Contradiction:

Measurement precisionVSEase of operation

Solution Approach 1:

The system performs self-service by automatically generating task definitions and configuring execution environments using historical data and machine learning models, eliminating the need for users to manually specify configuration parameters while maintaining precise configuration through automated resource recommendation engines

Inventive Principle:

Principle #25Self-service

Solution Approach 2:

The system introduces an intermediary automated configuration service that acts as a mediator between user intent and system execution, using historical data and machine learning to translate high-level user requirements into precise configuration parameters without requiring user expertise in container orchestration

Inventive Principle:

Principle #24Intermediary (Mediator)

3Adaptability or versatility

If manual configuration is required for each task launch, then configuration flexibility is maintained, but productivity decreases

Engineering Contradiction:

Improveconfiguration flexibilityVSAvoidtask launch speed

Core Design Contradiction:

Adaptability or versatilityVSProductivity

Solution Approach 1:

The system performs preliminary action by pre-configuring execution environments based on historical execution data and storing task definitions for future use, allowing users to launch tasks without manual configuration while maintaining precision through pre-computed resource allocations

Inventive Principle:

Principle #10Preliminary action

Solution Approach 2:

The system introduces dynamics by enabling flexible modification of pre-configured task definitions and providing adaptive resource allocation that adjusts to changing requirements, allowing users to maintain configuration flexibility through programmatic interfaces while achieving high productivity through automated baseline configurations

Inventive Principle:

Principle #15Dynamics

Data Source

PatentUS11487591B1Automatically configuring execution of a containerized application

Publication Date: 2022.11.01 AMAZON TECH INC

AI summary

Provided is a system for automatically generating a set of parameters that can be used to execute a user application on a cluster of compute instances on behalf of a user, where the set of parameters specifies the one or more container images that need to be executed as part of executing the user application. For example, the user may specify a set of container images that are part of the user application, and the system may automatically determine the parameters that define the computing environment in which the user application is to be executed, such as the resource allocation and networking configuration parameters, without the user having to provide such parameters to the system. These parameters can be packaged into the set of parameters (also referred to herein as a task definition), which can be used in future executions of the user application.