The patent badge is an abbreviated version of the USPTO patent document. The patent badge does contain a link to the full patent document.

The patent badge is an abbreviated version of the USPTO patent document. The patent badge covers the following: Patent number, Date patent was issued, Date patent was filed, Title of the patent, Applicant, Inventor, Assignee, Attorney firm, Primary examiner, Assistant examiner, CPCs, and Abstract. The patent badge does contain a link to the full patent document (in Adobe Acrobat format, aka pdf). To download or print any patent click here.

Date of Patent:
Jan. 21, 2025

Filed:

Oct. 20, 2021
Applicant:

Emc Ip Holding Company Llc, Hopkinton, MA (US);

Inventors:

Rômulo Teixeira de Abreu Pinho, Niterói, BR;

Vinicius Michel Gottin, Rio de Janeiro, BR;

Eduardo Vera Sousa, Niterói, BR;

Assignee:

EMC IP HOLDING COMPANY LLC, Hopkinton, MA (US);

Attorneys:
Primary Examiner:
Int. Cl.
CPC ...
G06F 9/50 (2006.01); G06F 9/48 (2006.01); G06F 11/34 (2006.01); G06F 16/901 (2019.01); G06F 18/22 (2023.01); G06N 3/02 (2006.01);
U.S. Cl.
CPC ...
G06F 9/505 (2013.01); G06F 9/48 (2013.01); G06F 9/4806 (2013.01); G06F 9/4843 (2013.01); G06F 9/4881 (2013.01); G06F 9/50 (2013.01); G06F 9/5005 (2013.01); G06F 9/5027 (2013.01); G06F 9/5055 (2013.01); G06F 9/5083 (2013.01); G06F 11/3409 (2013.01); G06F 16/9024 (2019.01); G06F 18/22 (2023.01); G06N 3/02 (2013.01); G06F 2209/501 (2013.01); G06F 2209/5019 (2013.01);
Abstract

Techniques described herein relate to systems and methods for workload placement based on subgraph similarity. Such techniques may include obtaining an encoded workload graph based on receiving a workload execution request; using the encoded workload subgraph to obtain encoded graphs representing previous workload executions, encoded subgraphs representing infrastructures on which the workload were executed, resource usage information, and execution metrics; using the encoded infrastructure subgraphs using subgraph similarity to identify candidate infrastructure subgraphs, using an ML model to predict an execution metric for an execution of the workload using the candidate; and selecting a best candidate infrastructure on which to execute the workload based on the predicted execution results.


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