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:
May. 29, 2018

Filed:

Nov. 16, 2014
Applicant:

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

Inventors:

Jonathan Dunne, Dungarvan, IE;

James P. Galvin, Jr., Georgetown, KY (US);

Shadi Ghaith, Dublin, IE;

Patrick J. O'Sullivan, Dublin, IE;

Hitham Ahmed Assem Aly Salama, Dublin, IE;

Attorneys:
Primary Examiner:
Assistant Examiner:
Int. Cl.
CPC ...
G06F 17/18 (2006.01); G06F 9/50 (2006.01); H04L 29/08 (2006.01); H04L 12/26 (2006.01); H04L 12/24 (2006.01); G06F 11/36 (2006.01); G06F 11/26 (2006.01); G06F 11/00 (2006.01);
U.S. Cl.
CPC ...
G06F 17/18 (2013.01); G06F 9/505 (2013.01); G06F 11/008 (2013.01); G06F 11/26 (2013.01); G06F 11/3672 (2013.01); H04L 41/147 (2013.01); H04L 43/50 (2013.01); H04L 67/10 (2013.01);
Abstract

An approach is provided for predicting system performance. The approach predicts system performance by identifying a Queuing Network Model (QNM) corresponding to a clustered system that handles a plurality of service demands using a plurality of parallel server nodes that process a workload for a quantity of users. A workload description is received that includes server demand data. Performance of the clustered system is predicted by transforming the QNM to a linear model by serializing the parallel services as sequential services, identifying transaction groups corresponding to each of the server nodes, and distributing the workload among the transaction groups across the plurality of nodes. The approach further solves analytically the linear model with the result being a predicted resource utilization (RU) and a predicted response time (RT).


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