Cloud Resource Orchestration for Multi-Instance Auto-Provisioning
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Solution Overview
Problem
Existing cloud platforms using Kubernetes Engine require manual user intervention for provisioning, limiting scalability and efficiency in deploying and managing resources, leading to increased defects, higher costs, and suboptimal simulation of complex production environments.
Innovation Solution
A system and method for dynamic resource configuration, management, and control that utilizes a novel API and loop-based functions to automate resource deployment, deletion, and monitoring, enabling simultaneous deployment of multiple resource instances within a single user session, and auto-scaling based on CPU usage.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If manual provisioning of cloud resources is used, then user control and simplicity are maintained, but scalability and provisioning speed are limited
Solution Approach 1:
The system enables self-service provisioning by allowing the automation system to automatically interpret test requirements, provision cloud resources, and manage test executions without requiring manual user intervention for each provisioning step. The platform autonomously scales resources based on test needs.
Solution Approach 2:
The system performs preliminary actions by pre-configuring resource templates, defining scaling policies in advance, and preparing resource allocation strategies before actual provisioning is needed. This enables rapid deployment when tests are initiated.
2Adaptability or versatility
If manual provisioning is used, then resource allocation simplicity is maintained, but the ability to simulate complex production environments is limited
Solution Approach 1:
The system introduces an intermediary layer of abstraction that translates complex production environment requirements into standardized provisioning requests. This mediator automatically handles the complexity of resource configuration, network setup, and environment orchestration, presenting simplified interfaces to users while managing underlying complexity.
3Reliability
If manual testing is used, then testing control is maintained, but product defects increase and customer satisfaction decreases
Solution Approach 1:
The system implements feedback mechanisms that continuously monitor test execution results, resource utilization metrics, and quality indicators. This feedback enables automatic adjustment of testing strategies, resource allocation, and scaling decisions to optimize both test quality and efficiency dynamically.
Data Source
AI summary
Distributed resource configuration, management, and control are provided through a frontend user interface, an application programming interface (API), and backend operations. Collections of configured and managed resources provide cloud services from a cloud environment to enterprise servers. A user provides resource definitions for resource types through the user interface. The backend operations translates the definitions into one or more low-level resource management API calls, which cause the resources to be configured, deployed, reserved/created, and controlled on one or more distributed devices. The configured resource of the distributed devices provide the cloud services to the enterprise servers. In an embodiment, a single user session via the user interface permits the user to deploy multiple instances of a resources within a corresponding environment.