View source for 17936793. MANAGED SOLVER EXECUTION USING DIFFERENT SOLVER TYPES simplified abstract (Amazon Technologies, Inc.)

=MANAGED SOLVER EXECUTION USING DIFFERENT SOLVER TYPES=


==Organization Name==

[[:Category:Amazon Technologies, Inc.|Amazon Technologies, Inc.]]


[[Category:Amazon Technologies, Inc.]]

==Inventor(s)==

[[:Category:Shreyas Vathul Subramanian of Herndon VA (US)|Shreyas Vathul Subramanian of Herndon VA (US)]][[Category:Shreyas Vathul Subramanian of Herndon VA (US)]]

[[:Category:Amey K Dhavle of Jersey City NJ (US)|Amey K Dhavle of Jersey City NJ (US)]][[Category:Amey K Dhavle of Jersey City NJ (US)]]

[[:Category:Guvenc Degirmenci of Kirkland WA (US)|Guvenc Degirmenci of Kirkland WA (US)]][[Category:Guvenc Degirmenci of Kirkland WA (US)]]

[[:Category:Kai Fan Tang|Kai Fan Tang]][[Category:Kai Fan Tang]]

[[:Category:Daniel Romero of Haddon Twp NJ (US)|Daniel Romero of Haddon Twp NJ (US)]][[Category:Daniel Romero of Haddon Twp NJ (US)]]

==MANAGED SOLVER EXECUTION USING DIFFERENT SOLVER TYPES - A simplified explanation of the abstract==


This abstract first appeared for US patent application 17936793 titled 'MANAGED SOLVER EXECUTION USING DIFFERENT SOLVER TYPES

==Simplified Explanation==

The multitenant solver execution service described in the abstract provides managed infrastructure for solving large-scale optimization problems. Here are some key points to explain this innovation:

* The service executes solver jobs on managed compute resources like virtual machines or containers.
* Compute resources can be automatically scaled based on client demand and are assigned to solver jobs in a serverless manner.
* Solver jobs can be initiated based on configured triggers.
* Users can select from different types of solvers, mix different solvers in a job, and translate a model from one solver to another.
* The service provides developer interfaces for running solver experiments, recommending solver types or settings, and suggesting model templates.
* Developers are relieved from managing infrastructure for running optimization solvers and can easily work with different solvers via a unified interface.

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### Potential Applications
* Supply chain optimization
* Resource allocation in cloud computing
* Production planning in manufacturing

### Problems Solved
* Eliminates the need for developers to manage infrastructure for running optimization solvers
* Provides a unified interface for working with different types of solvers
* Automates scaling of compute resources based on demand

### Benefits
* Increased efficiency in solving large-scale optimization problems
* Simplified workflow for developers working with solvers
* Cost-effective solution for optimization tasks

### Potential Commercial Applications
#### Optimized Resource Management in Cloud Computing

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### Possible Prior Art
One possible prior art for this technology could be cloud-based optimization platforms that offer similar services to manage and execute solver jobs on scalable compute resources.

### Unanswered Questions
#### How does the service handle security and privacy concerns when executing solver jobs on managed compute resources?
#### What level of customization is available for developers to fine-tune solver settings and configurations?


==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.



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