Real-Time Cloud Service Deployment for Resource Integration
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Solution Overview
Problem
Large and medium-sized enterprises often fail to optimally leverage available cloud-based resources due to the labor-intensive process of identifying and integrating them with in-house computing resources, leading to suboptimal performance in terms of cost, data throughput, and information governance.
Innovation Solution
A system utilizing a combination of an analyzer and script generator with machine learning capabilities automatically identifies and deploys cloud-based services that enhance performance priorities, such as data throughput and information governance, by generating declarative code for real-time deployment and integration, leveraging robotic process automation to automate the process.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of operation
If manual identification and integration of cloud-based services is performed, then labor intensity is high, but system complexity and integration challenges increase
Solution Approach 1:
The system enables self-service by automatically identifying cloud-based services, analyzing their performance metrics, generating deployment scripts, and integrating them with in-house computing resources without requiring manual intervention. The machine learning model autonomously evaluates service performance and the script generator automatically creates deployment configurations, transforming a manual process into an automated self-service system.
Solution Approach 2:
The patent replaces manual mechanical processes with automated computer-based systems. Instead of human operators manually identifying and integrating cloud services, the system uses machine learning algorithms to analyze service performance metrics and automated script generators to create deployment configurations, substituting human expertise with computational intelligence.
2Productivity
If cloud-based services are manually integrated, then integration time is long, but productivity is reduced
Solution Approach 1:
The system performs preliminary actions by pre-analyzing cloud-based service performance metrics, pre-generating deployment scripts, and pre-configuring integration parameters before actual deployment occurs. The machine learning model continuously evaluates service performance in advance, and the script generator prepares deployment configurations ahead of time, enabling rapid execution when deployment is needed.
Solution Approach 2:
The patent replaces time-consuming manual integration processes with automated computer-based systems that can rapidly analyze services, generate scripts, and execute deployments, dramatically reducing integration time and increasing productivity.
3Speed
If automated deployment scripts are generated, then deployment speed increases, but system reconfiguration complexity increases
Solution Approach 1:
The patent replaces complex manual reconfiguration tasks with automated script generation. The computer-based script generator analyzes current system state, cloud service requirements, and performance goals to automatically generate deployment scripts, eliminating the need for manual reconfiguration while maintaining precise control over system changes.
Solution Approach 2:
The system performs self-service by automatically analyzing its own state, identifying optimal cloud services, generating appropriate deployment scripts, and executing reconfigurations without external intervention, thereby increasing deployment speed while managing complexity internally.
Data Source
AI summary
Real-time resource deployment and integration can include determining one or more performance priorities for a user computer system based on a plurality of system-generated processing metrics. Based on the one or more performance priorities, a candidate cloud-based service can be determined among one or more previously unanalyzed cloud-based services identified by an automated watcher configured to search a plurality of communication network sites. The current performance of the user computer system can be compared to a potential performance of the user computer system were the candidate cloud-based service deployed. A script can be generated in response to determining, based on the comparing, that deploying the candidate cloud-based service improves performance of the user computer system with respect to the performance priorities. The script reconfigures the user computer system in real-time by automatically deploying the candidate cloud-based service.