US 11,789,774 B2
Optimization of workload scheduling in a distributed shared resource environment
Abhishek Malvankar, White Plains, NY (US); John M. Ganci, Jr., Raleigh, NC (US); Ashok Pon Kumar Sree Prakash, Bangalore (IN); and Umamaheswari Devi, Bangalore (IN)
Assigned to International Business Machines Corporation, Armonk, NY (US)
Filed by International Business Machines Corporation, Armonk, NY (US)
Filed on Feb. 22, 2021, as Appl. No. 17/180,948.
Prior Publication US 2022/0269531 A1, Aug. 25, 2022
Int. Cl. G06F 40/00 (2020.01); G06F 9/48 (2006.01); G06N 3/08 (2023.01); G06F 16/35 (2019.01); G06F 40/279 (2020.01); G06N 3/042 (2023.01); G06F 40/205 (2020.01)
CPC G06F 9/4881 (2013.01) [G06F 16/35 (2019.01); G06F 40/279 (2020.01); G06N 3/042 (2023.01); G06N 3/08 (2013.01); G06F 40/205 (2020.01)] 18 Claims
OG exemplary drawing
 
1. A computer system comprising:
a processing unit operatively coupled to memory;
an artificial intelligence (AI) platform operatively coupled to the processing unit, the AI platform configured with one or more tools to support optimization of workload scheduling, the one or more tools comprising:
a data manager configured to leverage one or more application artifacts related to a workload to identify host requirement data;
a natural language processing (NLP) manager configured to apply NLP to the data, the NLP to identify one or more host requirements corresponding to the one or more application artifacts;
a director configured to selectively identify one or more hosts in the computing environment compatible with the identified one or more host requirements; and
a scheduling manager operatively coupled to the director and configured to selectively schedule the workload responsive to the selective host identification, including assess compatibility of the workload with the identified one or more hosts; and
selective execution of the scheduled workload on at least one of the selectively identified hosts responsive to the assessment workload compatibility.
 
7. A computer program product to support optimization of workload scheduling, the computer program product comprising a computer readable storage medium having program code embodied therewith, the program code executable by a processor to:
leverage one or more application artifacts related to a workload to identify host requirement data;
apply NLP to the data, the NLP to identify one or more host requirements corresponding to the one or more application artifacts;
selectively identify one or more hosts in the computing environment compatible with the identified one or more host requirements;
selectively schedule the workload responsive to the selective host identification, including assess compatibility of the workload with the identified one or more hosts; and
selectively execute the scheduled workload on at least one of the selectively identified hosts responsive to the assessment workload compatibility.