System and method for optimization process repeatability in an on-demand computing environment

• US 8,036,921 B2
• Filed: 09/16/2004
• Issued: 10/11/2011
• Est. Priority Date: 09/16/2004
• Status: Expired due to Fees

• Current Assignee: International Business Machines Corporation
• Original Assignee: International Business Machines Corporation
• Inventors: Parija, Gyana R., Xi, Haifeng, Kumar, Tarun
• Primary Examiner(s): Jeanty; Romain
• Assistant Examiner(s): Fleischer; Mark A
• Application Number:
  US10/942,040
• Publication Number:
  US 20060058991A1
• Time in Patent Office: 2,581 Days
• Field of Search: 705/7.11
• US Class Current: 705/7.11
• CPC Class Codes: G06Q 10/04 Forecasting or optimisation specially adapted for administrative or management purposes, e.g. linear programming or "cutting stock problem" market predictions or forecasting for commercial activities G06Q30/0202
• CPC Class Codes: G06Q 10/063 Operations research, analysis or management

Abstract

A method (and system) of providing optimization repeatability in an on-demand computing environment removes variability in an optimization model instance and can be exemplarily implemented in a service architecture. The method and system receives a plurality of physical data instances, which are different representations of the same logical data model, and transforms the plurality of physical data instances into a normalized physical data instance, which can be combined with an optimization model to form a unique optimization model instance, thereby providing repeatability in solving optimization problems.

Claims

1. A method of providing optimization repeatability in an on-demand computing environment, comprising:

receiving a plurality of physical data instances;

transforming, using a processor of a computer, the plurality of physical data instances into a single normalized physical data instance; and

determining optimization model instances by combining said single normalized physical data instance with an optimization model,

wherein said normalized physical data instance includes a plurality of data elements,

wherein at least one data element of said plurality of data elements includes a collection of objects,

wherein said transforming comprises sorting said collection of objects by a predetermined object attribute, and

wherein said optimization model instances comprise a unique MPS (mathematical programming system) format input file.

2. The method according to claim 1, wherein said plurality of data elements comprises a hierarchical ordering.

3. The method according to claim 2, wherein said hierarchical ordering comprises a TreeMap structure.

4. A method for an on-demand computing environment, comprising:

receiving a plurality of physical data instances;

transforming, using a processor of a computer, said plurality of physical data instances into a single normalized physical data instance; and

obtaining an optimization model instance based on an optimization model and said normalized physical data instance,

wherein said normalized physical data instance includes a plurality of data elements,

wherein at least one data element of said plurality of data elements includes a collection of objects,

wherein said transforming comprises sorting said collection of objects by a predetermined object attribute, and

wherein said optimization model instances comprise a unique MPS (mathematical programming system) format input file.

5. A system for providing optimization repeatability in an on-demand computing environment, comprising:

a receiver that receives a plurality of data instances;

a data transformer that transforms the plurality of physical data instances into a single normalized physical data instance; and

a processor configured to determine optimization model instances by combining said single normalized physical data instance with an optimization model,

wherein said normalized physical data instance includes a plurality of data elements,

wherein at least one data element of said plurality of data elements includes a collection of objects,

wherein said data transformer sorts said collection of objects by a predetermined object attribute, and

wherein said optimization model instances comprise a unique mathematical programming system format input file.

6. The system according to claim 5, wherein said plurality of data elements comprises hierarchical ordering.

7. The system according to claim 6, wherein said hierarchical ordering comprises a TreeMap structure.

8. A system for providing optimization repeatability in an on-demand computing environment, comprising:

a receiver that receives a plurality of physical data instances;

a transformer that transforms said plurality of physical data instances into a single normalized physical data instance; and

a processor comprising a determiner that determines an optimized model instance based on an optimization model and said normalized physical data instance,

wherein said normalized physical data instance includes a plurality of data elements,

wherein at least one data element of said plurality of data elements includes a collection of objects,

wherein said transformer sorts said collection of objects by a predetermined object attribute,

wherein said optimization model instance comprises a unique MPS (mathematical programming system) format input file.

9. The system according to claim 8, further comprising:

a solver that receives said optimization model instance from said determiner and determines optimization based on said optimization model instance.

10. The system according to claim 8, further comprising:

a plurality of solvers,

wherein one of said plurality of solvers is selectable for receiving said optimization model instance from said determiner and generating an optimal solution based on said optimization model instance.

11. A system for providing optimization repeatability in an on-demand computing environment, comprising:

means for receiving a plurality of physical data instances and generating a single normalized physical data instance from said plurality of physical data instances;

a processor comprising means for determining a unique optimization model instance, based on said normalized physical data instance and an optimization model; and

means for receiving said unique optimization model instance and generating an optimal solution based on said unique optimization model instance,

wherein said normalized physical data instance includes a plurality of data elements,

wherein at least one data element of said plurality of data elements includes a collection of objects,

wherein said means for receiving a plurality of physical data instances and generating a single normalized physical data instance sorts said collection of objects by a predetermined object attribute,

wherein said optimization model instance comprises a unique MPS (mathematical programming system) format input file.

12. A tangible storage medium embodying a program of machine-readable instructions executable by a digital processing apparatus to perform a method of providing optimization process repeatability in an on-demand computing environment, the method comprising:

receiving a plurality of physical data instances;

transforming the plurality of physical data instances into a single normalized physical data instance; and

determining optimization model instances by combining said single normalized physical data instance with an optimization model,

wherein said normalized physical data instance includes a plurality of data elements,

wherein at least one data element of said plurality of data elements includes a collection of objects,

wherein said transforming comprises sorting said collection of objects by a predetermined object attribute, and

wherein said optimization model instances comprise a unique MPS (mathematical programming system) format input file.

13. A method of deploying computing infrastructure in which computer-readable code is integrated into a computer processor, and combines with said computer processor to perform a method of providing optimization process repeatability in an on-demand computing environment, said method comprising:

receiving a plurality of physical data instances;

transforming the plurality of physical data instances into a single normalized physical data instance; and

determining optimization model instances by combining said single normalized physical data instance with an optimization model,

wherein said normalized physical data instance includes a plurality of data elements,

wherein at least one data element of said plurality of data elements includes a collection of objects,

wherein said transforming comprises sorting said collection of objects by a predetermined object attribute, and

wherein said optimization model instances comprise a unique MPS (mathematical programming system) format input file.

14. The method according to claim 13, further comprising:

simultaneously deploying to a plurality of users of said computing system a change of service of at least one of said plurality of services.

15. The method according to claim 14, wherein said change of service includes at least one of upgrading, removing, altering, and replacing at least one of said plurality of services.