17936789. MULTI-TENANT SOLVER EXECUTION SERVICE simplified abstract (Amazon Technologies, Inc.)
Contents
- 1 MULTI-TENANT SOLVER EXECUTION SERVICE
- 1.1 Organization Name
- 1.2 Inventor(s)
- 1.3 MULTI-TENANT SOLVER EXECUTION SERVICE - 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
MULTI-TENANT SOLVER EXECUTION SERVICE
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)
MULTI-TENANT SOLVER EXECUTION SERVICE - A simplified explanation of the abstract
This abstract first appeared for US patent application 17936789 titled 'MULTI-TENANT SOLVER EXECUTION SERVICE
Simplified Explanation
The multitenant solver execution service provides managed infrastructure for solving large-scale optimization problems. Solver jobs are executed on managed compute resources such as virtual machines or containers, which can be automatically scaled based on client demand. Users can select from different types of solvers, mix solvers in a job, and translate models between solvers. The service offers developer interfaces for running solver experiments, recommending solver types/settings, and suggesting model templates. This service simplifies working with various solvers through a unified interface, eliminating the need for developers to manage infrastructure.
- Managed infrastructure for defining and solving large-scale optimization problems
- Execution of solver jobs on managed compute resources like virtual machines or containers
- Automatic scaling of compute resources based on client demand
- Selection of different solver types, mixing solvers, and translating models between solvers
- Developer interfaces for running solver experiments, recommending solver types/settings, and suggesting model templates
- Unified interface for working with different types of solvers
Potential Applications
The technology can be applied in industries such as logistics, supply chain management, finance, and manufacturing to optimize operations and resource allocation.
Problems Solved
1. Managing infrastructure for running optimization solvers 2. Working with different types of solvers efficiently
Benefits
1. Simplifies solving large-scale optimization problems 2. Allows for easy scaling of compute resources 3. Provides a unified interface for working with various solver types
Potential Commercial Applications
Optimization software companies, logistics firms, financial institutions, and manufacturing companies can benefit from this technology to improve efficiency and decision-making processes.
Possible Prior Art
One possible prior art could be cloud-based optimization platforms that offer similar services for solving large-scale optimization problems.
What is the impact of this technology on developer productivity?
The technology significantly improves developer productivity by eliminating the need to manage infrastructure for running optimization solvers. Developers can focus on solving problems and experimenting with different solver types without worrying about the underlying compute resources.
How does this technology ensure efficient resource allocation for solver jobs?
The technology automatically scales compute resources based on client demand, ensuring efficient resource allocation for solver jobs. This dynamic scaling helps optimize performance and cost-effectiveness for running large-scale optimization problems.
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.
