Industrial Analytics Framework for Automated Kubernetes Deployment
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Solution Overview
Problem
Existing analytics frameworks for deploying and executing analytics packages on distributed computing systems are inefficient, time-consuming, and complex, making it difficult for inexperienced users to utilize them effectively.
Innovation Solution
An improved analytics framework that automates the deployment and execution of analytics packages by generating a container image, allocating computing resources, and configuring service pods based on deployment parameters, using REST API calls to facilitate seamless integration and execution on a Kubernetes cluster.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of operation
If manual deployment processes are used for analytics packages on distributed computing systems, then users have control over deployment details, but deployment time and complexity increase significantly
Solution Approach 1:
The deployment framework enables self-service deployment by automatically generating service pods and allocating computing resources without requiring manual user intervention. The system autonomously processes deployment requests, manages container orchestration, and configures analytics packages, allowing users to simply submit deployment parameters while the framework handles all complex deployment operations automatically.
Solution Approach 2:
The framework performs preliminary actions by pre-configuring deployment templates, pre-allocating resource pools, and pre-compiling analytics packages into executable formats. Service pod templates and resource allocation policies are prepared in advance, enabling rapid deployment when requests are received without requiring real-time complex processing.
2Productivity
If automated deployment frameworks are implemented, then deployment speed and ease of use improve, but system complexity increases
Solution Approach 1:
The deployment framework acts as an intermediary layer between users and the underlying distributed computing system. It provides abstraction mechanisms that hide complex orchestration logic, resource management details, and container configuration from users. The framework translates simple deployment requests into complex backend operations, shielding users from complexity while maintaining high deployment efficiency.
Solution Approach 2:
The framework implements universal deployment capabilities that can handle multiple types of analytics packages, various computing resource types, and different deployment scenarios through a single unified interface. Service pod templates and resource allocation policies are designed to be universally applicable across different analytics workloads, reducing the need for multiple specialized deployment systems and thereby managing complexity.
3Use of energy by moving object
If computing resources are dynamically allocated based on deployment parameters, then resource utilization efficiency improves, but resource allocation complexity increases
Solution Approach 1:
The framework implements dynamic resource allocation that adapts computing resource distribution based on real-time deployment parameters and workload characteristics. Service pod configurations are dynamically adjusted according to resource availability, analytics package requirements, and system load conditions. This dynamic approach optimizes computing resource utilization by allocating resources precisely where and when they are needed, rather than using static pre-allocation schemes.
Data Source
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AI summary
A method includes receiving data characterizing an analytics package, and generating, by an analytics framework associated with a plurality of compute nodes, a container image associated with the analytics package and a unique identifier indicative of the container image. The container image is saved in a central container registry. The method further includes receiving, from a client, data characterizing deployment parameters associated with the deployment of the container image on the plurality of compute nodes and the unique identifier indicative of the container image. The method also includes generating at least one analytics service pod based on the deployment parameters and the unique identifier. The at least one analytics service pod includes the container image. The at least one analytics service pod is configured to execute the analytics package on one or more compute nodes of the plurality of compute nodes based on the deployment parameters. The deployment parameters include computing resource associated with execution of the at least one analytics service pod on the plurality of compute nodes.