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:
Nov. 09, 2021

Filed:

Jan. 03, 2020
Applicant:

International Business Machines Corporation, Armonk, NY (US);

Inventors:

Adrian Papacica, Carmel, CA (US);

Todd Weston Arnold, Charlotte, NC (US);

Barry Michael Graham, Silver Spring, MD (US);

Shenbaga Shankar, Pearland, TX (US);

Alexandra Urman, New York, NY (US);

Attorneys:
Primary Examiner:
Int. Cl.
CPC ...
H04L 12/24 (2006.01); H04L 29/08 (2006.01);
U.S. Cl.
CPC ...
H04L 41/5058 (2013.01); H04L 41/5041 (2013.01); H04L 67/10 (2013.01); H04L 41/5009 (2013.01);
Abstract

A microservices-management system intercepts a request for a cloud-based microservice sent by a microservices-architecture application. The system selects an optimal cloud-service provider from a group of candidate providers capable of delivering the microservice and then forwards the request to the optimal provider. The optimal provider is selected by drawing cognitive inferences from stored blockchain records that each describe a characteristic of a previous delivery of the requested service. Each record is generated by one of the candidate providers when delivering an instance of the microservice, regardless of whether the provider is in a trusted relationship with the application owner. The providers are barred by blockchain's intrinsic security features from altering or deleting previously stored blockchain records. Upon delivery of the service, the system compares the actual quality or cost of the delivery with predicted values in order to learn how to more effectively select optimal providers.


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