17936801. SOLVER EXECUTION SERVICE MANAGEMENT simplified abstract (Amazon Technologies, Inc.)
Contents
- 1 SOLVER EXECUTION SERVICE MANAGEMENT
- 1.1 Organization Name
- 1.2 Inventor(s)
- 1.3 SOLVER EXECUTION SERVICE MANAGEMENT - A simplified explanation of the abstract
- 1.4 Simplified Explanation
- 1.5 Potential Applications
- 1.6 Problems Solved
- 1.7 Benefits
- 1.8 Potential Commercial Applications
- 1.9 Possible Prior Art
- 1.10 Original Abstract Submitted
SOLVER EXECUTION SERVICE MANAGEMENT
Organization Name
Inventor(s)
Shreyas Vathul Subramanian of Herndon VA (US)
Amey K Dhavle of Jersey City NJ (US)
Guvenc Degirmenci of Kirkland WA (US)
Kai Fan Tang of Port Coquitlam (CA)
Daniel Romero of Haddon Twp NJ (US)
SOLVER EXECUTION SERVICE MANAGEMENT - A simplified explanation of the abstract
This abstract first appeared for US patent application 17936801 titled 'SOLVER EXECUTION SERVICE MANAGEMENT
Simplified Explanation
The abstract describes a multitenant solver execution service that provides managed infrastructure for solving large-scale optimization problems. The service executes solver jobs on managed compute resources, automatically scaling them based on client demand and assigning them in a serverless manner. Users can select from different types of solvers, mix them in a solver job, and translate models between solvers. Developer interfaces allow for running solver experiments, recommending solver types or settings, and suggesting model templates.
- Managed infrastructure for solving large-scale optimization problems
- Automatic scaling of compute resources based on client demand
- Selection of different types of solvers and mixing them in solver jobs
- Translation of models between solvers
- Developer interfaces for running solver experiments and recommending solver types or settings
Potential Applications
The technology can be applied in industries such as logistics, finance, manufacturing, and healthcare for optimizing complex processes and decision-making.
Problems Solved
The technology solves the problem of managing infrastructure for running optimization solvers, allowing developers to focus on solving problems rather than managing resources.
Benefits
The benefits of this technology include improved efficiency in solving large-scale optimization problems, flexibility in selecting and mixing different types of solvers, and ease of use through a unified interface.
Potential Commercial Applications
Potential commercial applications of this technology include offering optimization services to businesses in various industries, providing a platform for developers to solve complex optimization problems, and integrating optimization capabilities into existing software solutions.
Possible Prior Art
One possible prior art could be cloud-based optimization services that offer similar functionalities for solving large-scale optimization problems.
Unanswered Questions
How does the service ensure the security and privacy of sensitive data used in solver jobs?
The abstract does not mention specific measures taken by the service to ensure the security and privacy of data used in solver jobs. It would be important to understand the encryption protocols, access controls, and data handling practices implemented by the service to protect sensitive information.
What is the pricing model for using the solver execution service?
The abstract does not provide information on the pricing model for accessing and using the solver execution service. Understanding the cost structure, subscription plans, and any additional fees associated with using the service would be important for potential users to evaluate the economic feasibility of adopting the technology.
Original Abstract Submitted
A multitenant solver execution service provides managed infrastructure for defining and solving large-scale optimization problems. In embodiments, the service executes solver jobs on managed compute resources such as virtual machines or containers. The compute resources can be automatically scaled up or down based on client demand and are assigned to solver jobs in a serverless manner. Solver jobs can be initiated based on configured triggers. In embodiments, the service allows users to select from different types of solvers, mix different solvers in a solver job, and translate a model from one solver to another solver. In embodiments, the service provides developer interfaces to, for example, run solver experiments, recommend solver types or solver settings, and suggest model templates. The solver execution service relieves developers from having to manage infrastructure for running optimization solvers and allows developers to easily work with different types of solvers via a unified interface.
