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.
Patent No.:
Date of Patent:
Mar. 05, 2013
Filed:
Apr. 06, 2010
Tracy J. Kimbrel, Cortlandt Manor, NY (US);
Tarun Kumar, Mohegan Lake, NY (US);
Kevin D. Mckenzie, Poughkeepsie, NY (US);
Richard D. Prewitt, Jr., Poughkeepsie, NY (US);
Maxim Sviridenko, New York, NY (US);
Debra Tomkowid, Poughkeepsie, NY (US);
Tracy J. Kimbrel, Cortlandt Manor, NY (US);
Tarun Kumar, Mohegan Lake, NY (US);
Kevin D. McKenzie, Poughkeepsie, NY (US);
Richard D. Prewitt, Jr., Poughkeepsie, NY (US);
Maxim Sviridenko, New York, NY (US);
Debra Tomkowid, Poughkeepsie, NY (US);
International Business Machines Corporation, Armonk, NY (US);
Abstract
A system and method that provides an automated solution to obtaining quality scheduling for users of computing resources. The system, implemented in an enterprise software test center, collects information from test-shop personnel about test machine features and availability, test jobs, and tester preferences and constraints. The system reformulates this testing information as a system of constraints. An optimizing scheduling engine computes efficient schedules whereby all the jobs are feasibly scheduled while satisfying the users' time preferences to the greatest extent possible. The method and system achieves fairness: if all preferences can not be meet, it is attempted to evenly distribute violations of preferences across the users. The test scheduling is generated according to a first application of a greedy algorithm that finds an initial feasible assignment of jobs. The second is a local search algorithm that improves the initial greedy solution.