AUTO-ADJUSTING WORKER CONFIGURATION FOR GRID-BASED MULTI-STAGE, MULTI-WORKER COMPUTATIONS

1 . A method of improving the operational efficiency of segments of a data pipeline of a cloud based transactional processing system with multiple cloud based resources comprising:

a first step to virtually determine an approximation of a processing runtime of computations computing a value from transactions using potentially available resources of said cloud based transactional processing system for processing segments of a pipeline of data wherein said data comprising compensation and payment type data;

a second step to determine an actual processing runtime of computations computing a value from actual transactions using actual available resources using available resources for processing segments of a pipeline of data wherein said data comprising compensation and payment type data; and

a third step for adjusting a difference between the approximation of the runtime of said first step and the actual processing runtime of said second step by changing material parameters at least including the volume of transactions and available resources at particular segments of the pipeline, to produce an optimum result in adjusted processing runtime.

2 . The method of claim 1 , wherein the approximation of the runtime is based on parameters.

3 . The method of claim 1 , wherein if the difference between the approximation of the runtime and the actual runtime of the first and second stages is too large, a tuning is performed by changing the material parameters.

4 . The method of claim 1 , wherein the approximation is based on resources that are currently available.

5 . The method of claim 1 , wherein the first stage and the second stage are driven by user-defined rules.

6 . The method of claim 1 , wherein the third stage is driven by system-defined processes.

7 . A computer program in a computer readable storage medium for determining a pipeline for processing calculations of incentive based compensation comprising:

Program code for defining a first step to virtually determine an approximation of a processing runtime of computations computing a value from transactions using potentially available resources of said cloud based transactional processing system for processing segments of a pipeline of data wherein said data comprising compensation and payment type data;

Program code for defining a second step to determine an actual processing runtime of computations computing a value from actual transactions using actual available resources using available resources for processing segments of a pipeline of data wherein said data comprising compensation and payment type data; and

Program code for defining a third step for adjusting a difference between the approximation of the runtime of said first step and the actual processing runtime of said second step by changing material parameters at least including the volume of transactions and available resources at particular segments of the pipeline, to produce an optimum result in adjusted processing runtime.

8 . The method of claim 7 , wherein the approximation of the runtime is based on parameters.

9 . The method of claim 7 , wherein if the difference between the approximation of the runtime and the actual runtime of the first and second stages is too large, tuning is performed by changing the material parameters.

10 . The method of claim 7 , wherein the approximation is based on resources that are currently available.

11 . The method of claim 7 , wherein the first stage and the second stage are driven by user-defined rules.

12 . The method of claim 7 , wherein the third stage is driven by system-defined processes.

13 . A method of determining incentive based compensation, comprising:

Modeling a pipeline using a grid configuration for workers having a set of incentive based characteristics being associated with each of the workers,

Allocating resources for each of the workers in the grid configuration for processing with the incentive based characteristics through the modeled pipeline, and

Modeling a set of rules associated with the incentive based characteristics for the workers using resources of the grid configuration and figuring out the runtime for processing both the rule set and characteristics of the workers through the modeled pipeline.

14 . A method according to claim 13 , comprising:

Ascertaining an actual runtime of a pipeline using a grid configuration for workers having a set of incentive based characteristics being associated with each of the workers,

Determining an allocation of resources for each of the workers in the grid configuration for processing with the incentive based characteristics through the modeled pipeline, and

Determining a set of rules associated with the incentive based characteristics for the workers using resources of the grid configuration and the actual runtime for processing both the rule set and characteristics of the workers through the modeled pipeline.

15 . A method according to claim 14 , comprising

Comparing the modeled runtimes to the actual runtimes and changing the grid configuration to adjust the resources associated with the workers to allow the actual runtimes to be closer to the modeled runtimes.

16 . A method according to claim 15 , comprising

Changing a set of material parameters in the grid configuration to change the actual runtimes to enable the processing material characteristics and the set of the rules associated with the workers to a predetermined runtime.

17 . A method according to claim 16 , comprising

Changing memory allocations with the resources associated with the workers to change the actual runtimes.