US 11,916,807 B2
Evaluation framework for cloud resource optimization
Hagit Grushka, Beer-Sheva (IL); Rachel Lemberg, Herzliya (IL); Jeremy Samama, Herzliya (IL); Eliya Habba, Petah Tikva (IL); and Mohammad Salama, Herzliya (IL)
Assigned to MICROSOFT TECHNOLOGY LICENSING, LLC, Redmond, WA (US)
Filed by MICROSOFT TECHNOLOGY LICENSING, LLC, Redmond, WA (US)
Filed on Apr. 29, 2022, as Appl. No. 17/733,031.
Claims priority of provisional application 63/305,183, filed on Jan. 31, 2022.
Prior Publication US 2023/0246981 A1, Aug. 3, 2023
Int. Cl. H04L 47/762 (2022.01); H04L 47/78 (2022.01); H04L 43/50 (2022.01); H04L 47/70 (2022.01); H04L 47/80 (2022.01)
CPC H04L 47/762 (2013.01) [H04L 43/50 (2013.01); H04L 47/781 (2013.01); H04L 47/803 (2013.01); H04L 47/822 (2013.01)] 20 Claims
OG exemplary drawing
 
1. A method for evaluating a resource requirement recommendation, the method comprising:
receiving the resource requirement recommendation from a predictive model;
retrieving a system characteristic associated with currently available computing resources;
generating an activity dataset by sampling an active software deployment at the currently available computing resources for a predetermined timeframe;
generating a simulated computing environment based on the system characteristic and the activity dataset;
assigning an instance of a pending software deployment to the simulated computing environment according to the resource requirement recommendation; and
determining a level of resource utilization of the resource requirement recommendation by analyzing the instance of the pending software deployment within the simulated computing environment.