US 9,852,390 B2Granted

Methods and systems for intelligent evolutionary optimization of workflows using big data infrastructure

AssigneeGeneral Electric Company

InventorsKareem Sherif Aggour, Jenny Marie Weisenberg Williams

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Abstract

Methods and systems for optimizing the configuration and parameters of a workflow using an evolutionary approach augmented with intelligent learning capabilities using a Big Data infrastructure. In an embodiment, a Big Data infrastructure receives workflow input parameters, an objective function, a pool of initial configuration parameters, and completion criteria from a client computer, and then runs multiple instances of a workflow based on the pool of initial configuration parameters resulting in corresponding output results. The process includes storing the workflow input parameters and the corresponding output results, modeling the relationship between changes in the workflow input parameters and the corresponding output results, determining that optimal output results have been achieved, and then transmitting the optimal output and the input-output variable relationships results to the client computer.

Claims20

Dependent claims

A computer-implemented method for intelligent evolutionary optimization of a workflow, comprising:

receiving, by a Big Data infrastructure from a client computer, a run request comprising workflow input parameters, an objective function, a pool of initial configuration parameters, and completion criteria;
running, by the Big Data infrastructure, multiple parallel instances of a workflow based on the pool of initial configuration parameters resulting in corresponding parallel output results;
storing, by the Big Data infrastructure, the workflow input parameters and the corresponding output results;
modeling, by the Big Data infrastructure utilizing at least one of a statistical technique and a parametric technique, a relationship between changes in the workflow input parameters and the corresponding output results to gain insights into which input changes have the greatest impact and which input changes have the least impact on quality of the output results;
determining, by the Big Data infrastructure based on at least one of the objective function and the completion criteria as compared to results of the modeling, that optimal output results have been achieved;
tracking by the Big Data infrastructure the relationship between the change in the workflow input parameters and the corresponding output results and providing decisions regarding which workflow input parameters to use during additional workflow run requests which optimize the output results to reduce time, effort and computing expenses;
and
transmitting, by the Big Data infrastructure, the optimal output results and input-output variable relationships to the client computer.

5 more elements

A non-transitory computer readable medium storing instructions for intelligent evolutionary optimization of a workflow, in which the instructions are configured to cause a processor to:

receive, from a client computer, a run request comprising workflow input parameters, an objective function, a pool of initial configuration parameters, and completion criteria;
run multiple parallel instances of a workflow based on the pool of initial configuration parameters resulting in corresponding parallel output results;
store the workflow input parameters and the corresponding output results;
model, by utilizing at least one of a statistical technique and a parametric technique, a relationship between changes in the workflow input parameters and the corresponding output results to gain insights into which input changes have the greatest impact and which input changes have the least impact on quality of the output results;
determine, based on at least one of the objective function and the completion criteria as compared to results of the modeling, that optimal output results have been achieved;
track the relationship between the change in the workflow input parameters and the corresponding output results and providing decisions regarding which workflow input parameters to use during additional workflow run requests which optimize the output results to reduce time, effort and computing expenses;
and
transmit the optimal output results and input-output variable relationships to the client computer.

5 more elements

A system for the intelligent evolutionary optimization of a workflow, comprising:

a client server computer;
and
a Hadoop cluster comprising a plurality of server computers operably connected to the client server computer;
wherein the Hadoop cluster operates to:
receive, from the client computer, a run request comprising workflow input parameters, an objective function, a pool of initial configuration parameters, and completion criteria;
run multiple instances of a workflow many times in parallel based on the pool of initial configuration parameters resulting in corresponding parallel output results;
store the workflow input parameters and the corresponding output results;
model, by utilizing at least one of a statistical technique and a parametric technique, a relationship between changes in the workflow input parameters and the corresponding output results to gain insights into which input changes have the greatest impact and which input changes have the least impact on quality of the output results;
determine, based on at least one of the objective function and the completion criteria as compared to results of the modeling, that optimal output results have been achieved;
track the relationship between the change in the workflow input parameters and the corresponding output results and providing decisions regarding which workflow input parameters to use during additional workflow run requests which optimize the output results to reduce time, effort and computing expenses;
and
transmit the optimal output results and input-output variable relationships to the client computer.

9 more elements

Dependencies are inferred from the claim text.