US 11,755,926 B2
Prioritization and prediction of jobs using cognitive rules engine
Ritesh Kumar Gupta, Hyderabad (IN); Namit Kabra, Hyderabad (IN); Likhitha Maddirala, Hyderabad (IN); Eric Allen Jacobson, Arlington, MA (US); Scott Louis Brokaw, Groton, MA (US); and Jo Ramos, Grapevine, TX (US)
Assigned to International Business Machines Corporation, Armonk, NY (US)
Filed by INTERNATIONAL BUSINESS MACHINES CORPORATION, Armonk, NY (US)
Filed on Feb. 28, 2019, as Appl. No. 16/288,739.
Prior Publication US 2020/0279173 A1, Sep. 3, 2020
Int. Cl. G06F 9/48 (2006.01); G06F 9/50 (2006.01); G06N 3/08 (2023.01); G06N 5/025 (2023.01); H04L 41/5022 (2022.01); H04L 41/5019 (2022.01)
CPC G06N 5/025 (2013.01) [G06F 9/4881 (2013.01); G06F 9/5038 (2013.01); G06N 3/08 (2013.01); H04L 41/5022 (2013.01); G06F 2209/484 (2013.01); G06F 2209/5019 (2013.01); H04L 41/5019 (2013.01)] 17 Claims
OG exemplary drawing
 
1. A method for job prioritization, the method comprising:
receiving, by a cognitive rules engine, one or more jobs;
analyzing, using a learned metric of the cognitive rules engine, the one or more jobs, wherein the learned metric is based on a user inputted priority of the one or more jobs, a number of consumers of a result of the one or more jobs, a dependency of the one or more jobs, a number of computational resources required to run the one or more jobs, wherein the learned metric of the cognitive rules engine comprises:
using a result consumption analysis module to predict the number of consumers of the result of the one or more jobs;
using a dependency analysis module to determine the dependency of the one or more jobs on each other;
using an estimation of job start time module to determine a time required to finish a jobs in order to meet a service level agreement (SLA);
using a resource consumption analysis module to determine the number of computational resources required to run the one or more jobs;
using a user consumption analysis module to determine a consumer of the result of the one or more jobs; and
using a historical priority module to determine a historical priority of the one or more jobs or of a job with same characteristics; and
prioritizing the one or more jobs based on a result of the learned metric of the cognitive rules engine.