18096191. OPTIMIZING COMPONENTS FOR MULTI-CLOUD APPLICATIONS WITH DEEP LEARNING MODELS simplified abstract (International Business Machines Corporation)
Contents
OPTIMIZING COMPONENTS FOR MULTI-CLOUD APPLICATIONS WITH DEEP LEARNING MODELS
Organization Name
International Business Machines Corporation
Inventor(s)
PRANSHU Tiwari of SHARON MA (US)
Swarnalata Patel of MORRISVILLE NC (US)
NAVEEN Narayanaswamy of Bengaluru (IN)
Abhaya Kumar Sahoo of Khordha (IN)
OPTIMIZING COMPONENTS FOR MULTI-CLOUD APPLICATIONS WITH DEEP LEARNING MODELS - A simplified explanation of the abstract
This abstract first appeared for US patent application 18096191 titled 'OPTIMIZING COMPONENTS FOR MULTI-CLOUD APPLICATIONS WITH DEEP LEARNING MODELS
The abstract describes a computer-implemented method that involves ingesting application deployment data, generating a cloud application deployment predictor data structure, creating objective functions, optimizing between the objective functions, and generating a multi-cloud deployment map for the application.
- Ingesting application deployment data for an application
- Generating a cloud application deployment predictor data structure
- Creating objective functions for the predictor data structure
- Optimizing between the objective functions
- Generating a multi-cloud deployment map based on the optimization
Potential Applications: - Cloud computing - Application deployment optimization - Multi-cloud management
Problems Solved: - Efficient deployment of applications across multiple cloud environments - Predicting optimal deployment strategies - Enhancing resource utilization in cloud computing
Benefits: - Cost savings through optimized deployment - Improved performance of applications - Streamlined multi-cloud management
Commercial Applications: Title: "Enhanced Multi-Cloud Application Deployment Optimization" This technology can be utilized by cloud service providers, IT companies, and enterprises to streamline their application deployment processes, optimize resource allocation, and improve overall efficiency in managing multi-cloud environments.
Questions about the technology: 1. How does this method improve the efficiency of application deployment in multi-cloud environments? 2. What are the key factors considered in optimizing between the objective functions for cloud application deployment?
Original Abstract Submitted
In various examples, a computer-implemented method includes: ingesting, by one or more computing devices, application deployment data for an application; generating, by the one or more computing devices, a cloud application deployment predictor data structure for the application; generating, by the one or more computing devices, objective functions for the cloud application deployment predictor data structure for the application; optimizing, by the one or more computing devices, between the objective functions for the application; and generating, by the one or more computing devices, based on the optimizing between the objective functions, a multi-cloud deployment map for the application.
